JP2016212104A - Application breakdown method, application breakdown device and application breakdown program for gas consumption, and method, device and program for determining presence and absence of resident - Google Patents

Abstract

PROBLEM TO BE SOLVED: To break down a gas consumption in household for each application on the basis of the measurement data of a total gas consumption of an object house and an object dwelling.SOLUTION: A value is outputted for each measurement time for a predetermined time interval and measurement time by measurement (S1) in a trunk of a gas piping, which is the value where combined data of a time zone used by a gas tool is extracted as data of the continuous operation zone (S2) from measurement data of a total gas consumption obtained (S1) as data combined with a flow rate count number counting as one count for each predetermined flow rate, and the value multiplied by the flow rate count number and the flow rate is calculated. The flow rate of the gas in the trunk for each identifier of the measurement time is calculated (S3) using the value for each identifier of the measurement time, and after that, the application of the total gas consumption is classified into the hot water supply, the heating supply, and the kitchen supply (S4).SELECTED DRAWING: Figure 1

Description

  The present invention relates to an application decomposition method, an application decomposition apparatus, and an application decomposition program for gas consumption. More specifically, the present invention relates to a technique suitable for use in grasping the demand amount for each use of gas at home. The present invention also relates to a determination method, a determination device, and a determination program for a resident being away from home. More specifically, the present invention relates to a technique suitable for use in estimating whether or not a person is present in a residence or a dwelling unit.

  In the following description, [] may be used to clarify the unit.

  In order to search for optimal use of energy at home and to select heat source equipment, it is necessary to grasp the final demand for each application for each type of energy such as electricity and gas. However, for gas, installation of a flow meter with pipe cutting is burdensome to the customer, and it is laborious and costly, so actual surveys of individual devices are rarely conducted. In addition, there is still no good method for decomposing gas meter measurement values into consumption by use (specifically, for example, “hot water supply”, “kitchen”, and “heating (gas heater, etc.)”) .

  As a device for measuring the gas flow rate without cutting the gas pipe, for example, a permanent magnet that is mounted on a membrane gas meter provided in the gas pipe and periodically moves according to the gas flow inside the membrane gas meter. A periodic magnetic change accompanying the movement of the gas is detected by a magnetic sensor, and a pulse output corresponding to a predetermined gas flow rate (specifically, a unit metering volume of a gas meter) is detected based on the detection of the periodic magnetic change. There are some which output the count number every predetermined time (for example, Patent Document 1 and Patent Document 2).

  In the gas flow rate measuring apparatus as described above, the gas consumption at home is grasped as a count number every predetermined time, the predetermined time is, for example, 10 seconds, and the actual gas flow rate corresponding to one count is, for example, 1. 2 [L], 1.0 [L], and the like. In other words, in the gas flow rate measuring apparatus as described above, the gas consumption is output as a count number that is counted as one count for each predetermined flow rate at a predetermined time.

  Therefore, in the gas flow rate measuring device as described above, specifically, for example, when the predetermined time is 10 seconds and the actual flow rate per count is 1.2 [L], the gas flow rate measuring device is at intervals of 10 seconds. The gas consumption in increments of 1.2L is grasped.

  In addition, as a conventional method for disassembling the gas usage according to the usage, for example, the attribute information of the gas user such as the number of households, the number of owned gas appliances for kitchens, and the residence form is used. Obtain the amount of gas used for kitchens with different amounts used, and then use the total amount of gas used during periods when heating is not used as the sum of the amount used for kitchens and hot water. Next, use the correlation between the temperature of the hot water supply gas when the heater is not used and the temperature to obtain the hot water gas use amount when the heating is used, and then use the total gas usage for the kitchen. In addition, there is a method for obtaining a heating gas usage amount by subtracting a hot water supply gas usage amount (Patent Document 3).

As for electricity, it is considered to realize a service for watching elderly people living alone or their families by estimating the living state from the use of electricity (ie, power demand), or automatically controlling equipment such as air conditioning in the residence. In order to improve the operational efficiency and save electricity for the system performed in (1), there is an example in which it is determined whether the resident is at home or absent. Specifically, there are those that use door opening and closing, human sensors, CO ₂ sensors, etc., and electric power demand is one of the non-intrusive monitoring methods that do not require the installation of equipment in the house. Those using data have been developed (Non-patent Document 1).

  In connection with such non-intrusive monitoring, the low-voltage of next-generation wattmeters, which are called smart meters in Japan, which have a communication function and can check the power usage status (ie, power demand) in real time. Installation in a department (specifically, a general household) is being promoted (Non-Patent Document 2).

JP 2004-125634 A JP 2014-006058 A JP 2000-2221064 A

T.A. A. Nguyen and M.M. Aiello “Energy intelligent buildings based on user activity: A survey”, Energy and Buildings, Vol. 56, pp. 244-257, 2013 Ministry of Economy, Trade and Industry Smart Meter System Review Meeting “Problems and Responses Associated with the Promotion of Smart Meters”, [online], [Accessed April 14, 2016], Internet <URL: http: //www.meti.go .jp / committee / summary / 0004668 / pdf / 015_03_00.pdf>

  The measurement data obtained by the gas flow rate measuring devices of Patent Literature 1 and Patent Literature 2 is a flow rate count number that is output every predetermined time and counted as one count for each predetermined flow rate. However, for example, the gas consumption of a gas fan heater or gas stove is usually smaller than 1.2 [L / 10 seconds], so the actual flow rate of 1.2 [L] output at 10-second intervals is counted as one count. In the measurement as the flow rate count number, the change in the count value is sporadic (in other words, intermittent). Therefore, the flow rate count number as a measurement value corresponding to the gas consumption of each instrument such as a gas fan heater or gas stove is 0 or 1, even if these instruments are continuously used. For this reason, there is a problem that it is difficult to properly grasp the actual gas flow rate in the gas piping at each time, and it is difficult to properly grasp the actual gas flow rate at each time.

  In addition, in the method of disassembling gas usage according to application in Patent Document 3, attribute information of the gas user such as the number of households, the number of owned gas appliances for kitchens, and the residence form is required. It is difficult to obtain, and even if it is possible, it takes a lot of trouble. For this reason, it is hard to say that versatility is high. In addition, in the method for decomposing gas usage according to application in Patent Document 3, further information on temperature is required. However, collection of daily temperature data during the period of use decomposition for each area where the target household is located is very large. In this respect, it is difficult to say that the versatility is high, and depending on the granularity of the temperature data, the difference between the actual temperature at the location of the target household may increase. Therefore, it cannot be said that good accuracy is always ensured and reliability is high.

  Therefore, the present invention is the measurement data of the total gas consumption of the target house and the target dwelling unit without requiring related information and data, and the occurrence of the change in value is intermittently grasped at predetermined intervals. In addition, gas consumption usage decomposition method and usage decomposition that can decompose gas consumption at home with good accuracy based on measurement data group that is discretely grasped with a predetermined amount as a unit of change in value An object is to provide a device and a use decomposition program.

  Further, a route for sending a power demand value automatically read by a smart meter being installed as a next-generation power meter to an electric power company (specifically, a power transmission / distribution department) is called an “A route”. The power demand data obtained from the A route (referred to as “A route measurement value”) is output at intervals of 30 minutes and is a numerical value in increments of 100 Wh (in other words, in units of 100 Wh). Therefore, in the A route measurement value, the power demand that is less than 100 Wh as the actual power demand in 30 minutes is rounded down (in other words, not counted) as the measurement value at the time (30 minutes time zone), It is carried over to the measured value at the time (30 minutes).

Specifically, the A route measurement value output corresponding to the actual power demand is as shown in Table 1 below, for example. “Actual power demand” in Table 1 is a virtual value when an instantaneous value of power demand is temporarily measured, and is an integral value for 30 minutes.

  From the example shown in Table 1, there is no power demand in the A route measurement value even though there is actually power demand in the time division (30 minute time zone) where the power demand for 30 minutes is less than 100 Wh. Even if the difference in actual power demand is small (specifically, about 16 Wh) in continuous time segments (specifically, 11:00 and 11:30), route A It is confirmed that the measured value is as much as 100 Wh.

  For this reason, when it is determined whether the resident is at home or absent using the measured value of the A route as it is, there is a possibility that appropriate determination may not be performed, and it is difficult to perform determination with high accuracy. It is. In particular, in the time zone when the actual power demand in 30 minutes is less than 100 Wh, the measured value of A route is 0 Wh and 100 Wh, which are alternately output, compared to the actual power demand. The difference becomes a relatively large ratio, and misjudgment increases. In this regard, there is a tendency for power demand to decrease during times when devices with relatively large power consumption, such as air conditioners and microwave ovens, are not used due to switching to energy-saving devices. It is thought that it will increase.

  Therefore, the present invention does not require the installation of additional special equipment and related information and data, for example, the power demand of the target house or the target dwelling unit, such as the A route measurement value of the smart meter. Based on measurement data groups that are measurement data that are intermittently grasped about occurrence of a change in value at predetermined intervals and discretely grasped in terms of a predetermined amount as to a mode of change in value. It is an object of the present invention to provide a determination method, a determination device, and a determination program for a resident's absence from the home, which can estimate whether a person is present or not.

  In addition, each short time or specific time is independently determined (in other words, only a certain short time or specific time is extracted, and the situation before the certain short time or specific time is considered together. If you decide that you are away from home, you may have incorrectly determined that you are away from the situation where there are few electrical devices used or there is no change in the usage of electrical devices even if the resident is at home. There is a problem that you may end up.

  Therefore, the present invention aims to provide a method for determining the absence period that can estimate whether a person exists or does not exist in a residence or a dwelling unit without being affected by the situation for only a short time. And

  In the description of the present invention, a gas pipe that is sent to a house or a household of a household (household), which is a unit for decomposing gas consumption according to use, is called a “main trunk” and the flow rate of gas flowing through the main trunk is expressed as “ It is called “main gas flow rate” or “total gas consumption”, and the present invention decomposes the total gas consumption according to the application. In a house or a dwelling unit, indoor piping branched from the main trunk is connected to gas appliances for hot water supply, kitchen use, and heating.

  The gas consumption usage decomposition method according to the present invention is a measurement time at a predetermined time interval and a value output at each measurement time by measurement in the main of the gas pipe, and is counted as one count for each predetermined flow rate. From the measurement data of the total gas consumption acquired as combination data with the flow rate count to be performed, the combination data of the time zone when the gas appliance connected to the indoor piping branched from the main is used is the data of the continuous operation section The lower limit is a value obtained by multiplying the cumulative value of the flow rate count at each measurement time in the continuous operation section and the predetermined flow rate, and the upper limit is a value obtained by adding the predetermined flow rate to the multiplied value. The total length from the start point to the end point of the continuous operation section is the shortest and the total gas consumption measured at each measurement time is A continuous line is estimated after the gas flow velocity in the main trunk is calculated as the slope of the straight line between each measurement time at the measurement time. A continuous heating operation section is identified based on the process for determining whether the data of the section is winter or not and whether the state where the gas flow rate is in the predetermined range continues for a predetermined time. The sum of the heating base flow rate and the hot water supply minimum flow rate, and the processing in which the minimum value of the gas flow velocity in all the heating continuous operation intervals specified in the continuous operation interval data is set as the heating base flow velocity. And a process in which the gas flow rate is compared, a process in which the minimum hot water flow rate and the gas flow rate are compared, and at least one of the kitchen minimum flow rate and the intermittent heating maximum flow rate Magnitude with flow velocity There By the process to be compared are executed, the total gas consumption applications so that the hot water supply amount is classified heating amount, and the kitchen minute.

  Further, the gas consumption usage decomposition apparatus according to the present invention is a measurement time at a predetermined time interval and a value output at each measurement time by measurement in the main of the gas pipe, and counts as one count for each predetermined flow rate. From the measurement data of the total gas consumption acquired as the combination data with the flow rate count, the combination data of the time zone in which the gas appliance connected to the indoor piping branched from the main is used is the data of the continuous operation section And a value obtained by multiplying the accumulated value of the flow rate count number at each measurement time in the continuous operation section and the predetermined flow rate as a lower limit and a value obtained by adding the predetermined flow rate to the multiplied value Total gas consumption measured at each measurement time with the shortest total length from the start point to the end point of the continuous operation section. A means for estimating a broken line in which the sum of the total gas consumption at each measurement time and the estimated total gas consumption coincide with each other and calculating the gas flow velocity in the main trunk as the slope of the straight line between each measurement time in the broken line, and continuous operation Determine whether or not the data of the section is winter, identify the continuous heating operation section based on whether or not the state where the gas flow rate is in a predetermined range continues for a predetermined time and continue The minimum value of the gas flow velocity in all the heating continuous operation intervals specified in the operation interval data is set as the heating base flow velocity, and the sum of the heating base flow velocity and the minimum hot water supply flow velocity and the gas flow velocity are set. By comparing the magnitudes of the minimum hot water flow rate and the gas flow rate, and comparing the gas flow rate with at least one of the minimum kitchen flow rate and the intermittent heating maximum flow rate, Total consumption The amount applications hot water fraction of, so that a means for classifying the heating amount, and the kitchen minute.

  Further, the gas consumption usage decomposition program of the present invention is a measurement time at a predetermined time interval and a value output at each measurement time by measurement in the main of the gas pipe, and counts as one count for each predetermined flow rate. From the measurement data of the total gas consumption acquired as the combination data with the flow rate count, the combination data of the time zone in which the gas appliance connected to the indoor piping branched from the main is used is the data of the continuous operation section And a value obtained by multiplying the accumulated value of the flow rate count number at each measurement time in the continuous operation section and the predetermined flow rate as a lower limit, and a value obtained by adding the predetermined flow rate to the multiplied value The gas that is in the region where the upper limit is the maximum and the total length from the start point to the end point of the continuous operation section is the shortest and is measured at each measurement time A process for estimating a broken line in which the total amount of consumption and the total amount of gas consumed at each measurement time coincide with each other, and calculating a gas flow velocity in the main trunk as a slope of a straight line between the measurement times in the broken line; It is determined whether or not the data of the continuous operation section is in winter, and the heating continuous operation section is specified based on whether or not the state where the gas flow rate is in a predetermined range continues for a predetermined time. In addition, the minimum value of the gas flow velocity in all the heating continuous operation intervals specified in the data of the continuous operation interval is set as the heating base flow velocity, and the sum of the heating base flow velocity and the minimum hot water supply flow velocity and the gas flow velocity By comparing the size, comparing the size of the hot water supply minimum flow rate with the gas flow rate, and comparing the size of the gas flow rate with at least one of the minimum kitchen flow rate and the maximum intermittent heating flow rate , Total consumption applications hot water amount, and are heated fraction, and a process of classifying the kitchen fraction to be performed by the computer.

  Therefore, according to the application decomposition method, the application decomposition apparatus, and the application decomposition program for these gas consumption amounts, the measurement time at a predetermined time interval and the value output at each measurement time are counted as 1 for each predetermined flow rate. Combined data with the flow count number to count as, that is, data in which the change in value of a predetermined time interval and a predetermined flow rate step is sporadic (in other words, intermittent) and discrete, or in other words, value The gas flow rate at each measurement time is calculated based on data in which the occurrence of the change is sporadic (intermittent) in a time series and the change mode of the value is discrete.

  According to the application decomposition method, the application decomposition apparatus, and the application decomposition program of these gas consumptions, the usage of the total gas consumption is further divided into hot water supply, heating and kitchen, based on the calculated gas flow velocity. being classified.

  Further, the gas consumption consumption decomposition method, the application decomposition apparatus, and the application decomposition program according to the present invention first perform a process for determining whether or not the data of the continuous operation section is winter after the gas flow velocity is calculated. The process in which the heating continuous operation section is specified may be performed next, and the process in which the heating base flow rate is set may be performed subsequently. In this case, whether or not the heating gas appliance is used is determined first, and when the heating gas appliance is used, the heating gas consumption is first separated from the total gas consumption, and then Classification of gas consumption for hot water and kitchen is performed.

  In addition, the determination method for the absence of a resident at home according to the present invention includes a measurement time of a predetermined time interval and a power demand amount that is a value output at each measurement time and is measured using a predetermined power amount as a measurement unit. Combined data for a predetermined time period is extracted as analysis target section data from the measurement data of power demand acquired as combination data, and the cumulative value of power demand at each measurement time in the analysis target section is used as the lower limit. The total length from the start point to the end point of the analysis target section is the shortest and exists in the area whose upper limit is the value obtained by adding the predetermined power amount to the accumulated value of the power demand amount at each measurement time, and each measurement time A line that matches the total power demand measured in step 1 and the total power demand estimated at each measurement time is estimated, and the power demand is calculated as the slope of the straight line between each measurement time. The power demand rate included in the predetermined period before the measurement time that is the target of the absence of home is calculated using the power demand speed statistic in the predetermined period as the feature amount, The feature amount is used to determine whether the resident is away from home.

  In addition, the determination device for the absence of the resident at home according to the present invention includes a measurement time of a predetermined time interval and a power demand amount that is a value output at each measurement time and is measured using a predetermined power amount as a measurement unit. From the measurement data of power demand obtained as combination data, combination data in a predetermined time zone is extracted as data of the analysis target section, and the cumulative value of power demand at each measurement time in the analysis target section is set as the lower limit. The total length from the start point to the end point of the analysis target section is the shortest and exists in the area whose upper limit is the value obtained by adding the predetermined power amount to the accumulated value of the power demand amount at each measurement time, and each measurement time The power demand rate is estimated as the slope of the straight line between each measurement time on the line that estimates the line that matches the total power demand measured in Calculating at least a feature of a statistic of the power demand rate in the predetermined period using a power demand rate included in a predetermined period before the measurement time that is a target for determination of absence from home; Means for determining the absence of the resident by using the quantity.

  Further, the resident absence-at-home determination program according to the present invention includes a measurement time at a predetermined time interval and a power demand amount that is a value output at each measurement time and measures a predetermined power amount as a measurement unit. From the measurement data of power demand obtained as combination data, combination data in a predetermined time zone is extracted as data of the analysis target section, and the cumulative value of power demand at each measurement time in the analysis target section is set as the lower limit. The total length from the start point to the end point of the analysis target section is the shortest and exists in the area whose upper limit is the value obtained by adding the predetermined power amount to the accumulated value of the power demand amount at each measurement time, and each measurement time Estimate a line that matches the total power demand measured at each measurement time and the total power demand at each measurement time. The calculation of the speed and the power demand speed included in the predetermined period before the measurement time, which is the target of the absence from home, are used as the feature amount to calculate the statistical amount of the power demand speed in the predetermined period and at least A process for determining whether the resident is away from home using the feature amount is performed by a computer.

  Therefore, according to the determination method, determination device, and determination program for the absence of the resident at home, the measurement time at a predetermined time interval and the value output at each measurement time, and the predetermined power amount as a measurement unit Combined data with the power demand to be measured, that is, data in which the change in value of a predetermined time interval and a predetermined power step is sporadic (in other words, intermittent) and discrete, or in other words, a value The power demand rate at each measurement time is calculated based on data in which the occurrence of the change is sporadic (intermittent) in the time series and the manner of change in the value is discrete.

  According to the determination method, determination apparatus, and determination program for the absence of the resident at home, it is further estimated whether a person or a person is present in the residence or the dwelling unit based on the calculated power demand rate.

  Moreover, the determination method, determination apparatus, and determination program of the resident absence at home of this invention may make it the measurement data of electric power demand amount be data output from A route | root of a smart meter. In this case, the A route measurement value of the smart meter is used as measurement data for the power demand.

  In the absence period determination method of the present invention, the power demand rate for each time is calculated based on the power demand measurement data, and the power demand rate included in the predetermined period before the determination target time is used. A statistical amount of the power demand rate in the predetermined period is calculated as a feature amount, and at least the feature amount is used to determine whether the resident is away from home for each determination target time, and has a predetermined time length. When all the determination results for each determination target time corresponding to the determination period moving in the axial direction are absent, it is determined that the determination period is absent during the determination period, and the start time of one determination period determined to be absent When the absence start time and the end time are specified as the absence end time, or the start time of the first determination period among a plurality of determination periods determined to be absent continuously is the absence start time It is to be identified as the absence of the end time the time of the end of the last judgment period.

  Therefore, according to this method of determining the absence period, it is possible to determine absence at home independently for each determination target time (that is, without considering the situation at the time prior to the determination target time). In a situation where there are few electrical devices used for a short time or there is no change in the usage mode of the electrical devices even though the resident is at home (in other words, the change in the power demand rate is small) It is possible to prevent an erroneous determination that the person is absent (in the situation).

  According to the application decomposition method, the application decomposition apparatus, and the application decomposition program of the gas consumption of the present invention, the occurrence of the value change is sporadic (intermittent) in time series, and the aspect of the value change is discrete. Since the flow rate of the gas at each measurement time can be calculated based on certain data, it becomes possible to improve the reproducibility of the actual state of the total gas consumption.

  According to the application decomposition method, the application decomposition apparatus, and the application decomposition program of the gas consumption of the present invention, the usage of the total consumption of the gas is further divided into the hot water supply and heating based on the flow velocity of the gas that is reproduced well. Therefore, it is possible to improve the reliability of the gas consumption as an application decomposition technique by ensuring good accuracy of the application decomposition of the total consumption of gas.

  In addition, the application decomposition method, the application decomposition apparatus, and the application decomposition program of the gas consumption according to the present invention are such that after determining whether or not the data of the continuous operation section is winter, the heating continuous operation section is specified. When the heating base flow rate is set, when heating gas appliances are used, the gas consumption for hot water and the kitchen is consumed after the gas consumption for heating is first divided from the total gas consumption. Since the amount is classified, it is possible to accurately classify the gas consumption for heating, and also accurately classify the gas consumption for the remaining hot water and kitchen. It becomes possible.

  In addition, according to the determination method, determination apparatus, and determination program for resident absence from home according to the present invention, the occurrence of a change in value is sporadic (intermittent) in time series, and the aspect of the change in value is discrete. Since the power demand rate at each measurement time can be calculated based on the data, it is possible to improve the reproducibility of the actual power demand.

  According to the determination method, determination apparatus, and determination program for the resident absence from home according to the present invention, the presence or absence of a person in the dwelling or the dwelling unit is further based on the power demand speed at which the actual situation is well reproduced. Therefore, it is possible to ensure good accuracy in determining whether the resident is away from home and to improve reliability as a technique for determining whether the resident is away from home.

  Further, according to the absence period determination method of the present invention, in a situation where there are few electrical devices to be used for a short time or there is no change in the usage mode of the electrical devices (in other words, a change in the power demand rate is small). It is possible to prevent an erroneous determination that the person is absent, and to improve the reliability as a method for determining the absence period.

It is a flowchart which shows an example of embodiment of the usage decomposition method of the gas consumption of this invention. It is a functional block diagram of the usage decomposition apparatus of the gas consumption implement | achieved by the said program in the case of implementing the usage decomposition method of the gas consumption of embodiment using the usage decomposition program of a gas consumption. It is a figure explaining the creation method of the continuous data of embodiment, and is a figure explaining the method of estimating a flow velocity based on the flow volume count number as measurement data. It is a use determination chart explaining an example of the algorithm used in the use decomposition | disassembly of gas consumption. It is a flowchart (S1 thru | or S3) which shows an example of embodiment of the determination method of the resident absence of home of this invention. It is a flowchart (S4 thru | or S11) which shows an example of embodiment of the determination method of the resident absence of home of this invention. It is a functional block diagram of the determination apparatus of a resident's absence at home implement | achieved by the said program in the case of implementing the determination method of a resident's absence at home using the determination program of a resident's absence from home of embodiment. It is a figure explaining the creation method of the continuous data of embodiment, and is a figure explaining the method of estimating an electric power demand speed based on the electric power demand amount as measurement data. It is a figure explaining the method of estimating the shortest broken line as a true accumulated demand curve. It is a figure explaining an example of the method of specifying a absentee start time and absentee end time, and the method of determining an absent period.

  Hereinafter, the configuration of the present invention will be described in detail based on an example of an embodiment shown in the drawings.

  FIG. 1 to FIG. 4 show an example of embodiments of the gas consumption consumption decomposition method, the application decomposition apparatus, and the application decomposition program according to the present invention.

The gas consumption usage decomposition method of the present embodiment is a measurement time at a predetermined time interval and a value output at each measurement time by measurement (S1) in the main of the gas pipe, and for each predetermined flow rate _fu . Combination of time periods in which gas appliances connected to indoor piping branched from the main trunk are used from the measurement data of the total gas consumption acquired (S1) as combination data with the flow rate count counted as one count Data is extracted as data of the continuous operation section (S2), and a value obtained by multiplying the accumulated value of the flow rate count at each measurement time in the continuous operation section by the flow rate _fu is set as the lower limit and the flow rate is multiplied by the multiplied value. gas-General measured in it besides the measurement time is the shortest reaching a value obtained by adding the f _u from the start point of the present and continuous operation period in the region of up to an end point Is estimated broken line and the sum of the total gas consumption of each measurement time is estimated to be the sum of consumption matches the flow velocity y _t of the gas in the main trunk as the slope of the straight line between the measurement time of the polygonal line are calculated above (S3), the processing data of the continuous operation period is determined whether or not winter and (S4-1), the state flow rate y _t of the gas is predetermined range over a predetermined time continuity minimum and processing (S4-2) the heating the continuous operation period is identified based on whether the flow velocity y _t of the gas in all the heating the continuous operation period identified in the data of the continuous operation period process value is set as the heating base flow rate and (S4-3), the processing size is compared with the sum and gas flow rate y _t the heating base flow rate and the hot water minimum flow rate as (S4-4), hot water the size is compared with the velocity y _t of the minimum flow rate and gas And management (S4-5), at least one of a gas flow rate y of one of the process (S4-6) the data of the continuous operation period is determined whether the summer, the kitchen minimum flow rate and intermittent heating maximum flow rate By executing the process (S4-7) in which the magnitude is compared with _t , the usage of the total gas consumption is classified into hot water supply, heating, and kitchen (S4). (See FIG. 1).

In the present embodiment, in the process of S3, the identifier t of the measurement time for each identifier t (where t = 1, 2, 3,..., T) given in chronological order of the measurement time in the continuous operation section. A value x _t obtained by multiplying the flow rate count number at the measurement time corresponding to and the flow rate f _u is calculated, and the value x _t for each identifier t of the measurement time is used to solve the optimization problem shown in Equations 1 to 3. flow rate y _t of the gas in the main trunk as the value y _t for each identifier t of the measurement time by either is released for out is to be calculated.

The gas consumption usage decomposition method may be implemented by a gas consumption usage decomposition apparatus. Applications cracker 10 of gas consumption in the present embodiment, one count a value that is output to each measurement time and the measurement time of the predetermined time intervals by measuring the main trunk of the gas pipe at every predetermined flow rate f _u As a means for receiving the measurement data of the total gas consumption acquired as combination data with the flow count number to be counted as the gas, the gas connected to the indoor pipe branched from the main trunk from the measurement data The decomposition section extraction unit 11b as means for extracting the combination data of the time zone in which the appliance is used as the data of the continuous operation section, the accumulated value of the flow count number at each measurement time in the continuous operation section, and the flow rate _fu . present and the start of the continuous operation period of the value obtained by adding the flow rate f _u in the region of up to a value obtained by multiplying the addition to the multiplying value was lower limit Estimate a polygonal line that has the shortest total length to the end point and that matches the total gas consumption measured at each measurement time and the total gas consumption estimated at each measurement time. determining the shortest polygonal line creating unit 11c as a means to calculate the flow rate y _t of the gas in the main trunk as the slope of the straight line between each measurement time, whether the data of the continuous operation period is winter at the flow rate of the gas y _The heating continuous operation section is specified based on whether or not the state where _t is in the predetermined range continues for a predetermined time, and among all the heating continuous operation sections specified in the data of the continuous operation section sets the minimum value of the flow velocity y _t of the gas in the heating base flow rate, compares the magnitude of the flow velocity y _t sum and gas and heating the base flow rate and the hot water minimum flow rate, the flow rate of the hot water minimum flow rate and gas y the ratio of the size of the _t And, by the data of the continuous operation period, it is determined whether the summer, further comparing the magnitude of the flow velocity y _t of at least one gas of a kitchen minimum flow rate and intermittent heating maximum flow rate, A use decomposing unit 11d as means for classifying the use of the total gas consumption into hot water, heating, and kitchen.

In the present embodiment, the shortest broken line creation unit 11c sets the measurement time for each identifier t (where t = 1, 2, 3,..., T) given in the time series order of the measurement time in the continuous motion section. A value x _t obtained by multiplying the flow rate count number at the measurement time corresponding to the identifier t and the flow rate f _u is calculated, and the optimization shown in Equations 1 to 3 is performed using the value x _t for each identifier t of the measurement time. and so as to calculate the flow rate y _t of the gas in the main trunk as the value y _t for each identifier t of the measurement time by solving any of the problems.

  The gas consumption usage decomposition method and the gas consumption usage decomposition apparatus can also be implemented and realized by executing a gas consumption usage decomposition program on a computer. Here, a case will be described in which a gas consumption usage decomposition apparatus is implemented by executing a gas consumption usage decomposition program on a computer and a gas consumption usage decomposition method is implemented.

  FIG. 2 shows an overall configuration of a computer 10 (which is also a gas consumption usage decomposition apparatus 10 in the present embodiment) for executing the gas consumption usage decomposition program 17 of the present embodiment. The computer 10 (gas consumption usage decomposition apparatus 10) includes a control unit 11, a storage unit 12, an input unit 13, a display unit 14, and a memory 15, which are connected to each other by a signal line such as a bus. .

  The control unit 11 performs calculation related to the control of the entire computer 10 and the decomposition of the usage of the total gas consumption by the usage decomposition program 17 of the gas consumption stored in the storage unit 12. Processing device).

  The storage unit 12 is a device that can store at least data and programs, and is, for example, a hard disk.

  The input unit 13 is an interface (that is, an information input mechanism) for giving at least an operator's command and various information to the control unit 11, and is, for example, a keyboard or a mouse. For example, a plurality of types of interfaces such as a keyboard and a mouse may be provided as the input unit 13.

  The display unit 14 performs drawing / display of characters, figures, images, and the like under the control of the control unit 11 and is, for example, a display.

  The memory 15 serves as a memory space that is a work area when the control unit 11 executes various controls and operations, and is, for example, a RAM (abbreviation of Random Access Memory).

The control unit 11 of the computer 10 (denoted as “gas consumption usage decomposition device 10”) executes a gas consumption usage decomposition program 17 to thereby execute a predetermined measurement by measurement in the main of the gas pipe. input of measurement data of the measuring time and the measuring a time value output at every predetermined flow rate f obtained total gas consumption as a combination data of the flow rate count number counted as one count for each _u intervals The data receiving unit 11a that performs the process of receiving the data, and the process of extracting, from the measurement data, the combination data of the time zones in which the gas appliances connected to the indoor pipe branched from the main trunk are used as the data of the continuous operation section lower and decomposing section extracting section 11b, and a value obtained by multiplying the accumulated value of the flow rate counts per measurement time and the flow rate f _u in the continuous operation period General extending from the start point of the multiplying allowed value to flow f _u values exist in the region of up to and continuous operation period plus the ending point is measured in it besides the measurement time is the shortest with the calculating the flow rate y _t of the gas in the main trunk as the slope of the straight line between the measurement time of the polygonal line by estimating a line the sum and matches the total gas consumption of each measurement time is estimated to be the sum of the total gas consumption and shortest polygonal line creating unit 11c which performs processing for the data of the continuous operation period, it is determined whether the winter, state velocity y _t of the gas is predetermined range continues over a predetermined time the minimum value of the flow velocity y _t of the gas in all the heating the continuous operation period identified in the data of the continuous operation period with specifying the heating continuous operation period set as heating the base flow rate based on the dolphin whether, Heating Base flow rate Comparing the magnitude of the flow velocity y _t of the sum of the hot water supply minimum flow rates and gas, comparing the magnitude of the flow velocity y _t of the hot-water minimum flow rate and gas, whether or not the data of the continuous operation period is summer determination and, further, by comparing the magnitude of at least one gas flow rate y _t of the kitchen minimum flow rate and intermittent heating maximum flow rate, the hot water supply amount of use of the total gas consumption, heating component, and kitchen The application disassembling unit 11d that performs the processing for classifying the minutes is configured.

In the present embodiment, the shortest broken line creation unit 11c sets the measurement time for each identifier t (where t = 1, 2, 3,..., T) given in the time series order of the measurement time in the continuous motion section. A value x _t obtained by multiplying the flow rate count number at the measurement time corresponding to the identifier t and the flow rate f _u is calculated, and the optimization shown in Equations 1 to 3 is performed using the value x _t for each identifier t of the measurement time. and to perform the processing to calculate the velocity y _t of the gas in the main trunk as the value y _t for each identifier t of the measurement time by solving any of the problems.

  And as implementation of the usage decomposition method of gas consumption, first, measurement of gas total consumption is performed and measurement data is acquired (S1).

  The total gas consumption is measured by measuring the main gas flow rate as a count number counted as one count for each predetermined flow rate, and outputting the count number as an electrical signal, for example, every predetermined time. If there is, it is not limited to a specific method or device, and a method or device capable of performing the measurement as described above is appropriately selected.

  Specifically, for example, the above-described gas flow rate measuring device (for example, Japanese Patent Application Laid-Open No. 2004-125634 and Japanese Patent Application Laid-Open No. 2014-6058) attached to a membrane gas meter can be used as an example. Alternatively, every time a certain amount of gas (for example, a unit metering volume of a gas meter) passes through the gas meter, a periodic operation sound or vibration generated from the gas meter due to the operation cycle is detected, and the number of times is determined as a count number. A device that outputs data every time can be used.

  In the measurement of the total gas consumption (main gas flow rate), the actual flow rate per count as the above-mentioned “predetermined flow rate” is determined, for example, according to the unit metering volume of the gas meter provided in the main unit. . Specifically, for example, when the unit metering volume of the gas meter is 1 [L], 1 [L] gas actually flows for every one count output from the gas flow rate measuring device (in other words, consumption If the unit metering volume of the gas meter is 1.2 [L], 1.2 [L] gas is actually output for every one count output from the gas flow rate measuring device. It means that it flowed.

  In the measurement of the total gas consumption, the time interval for outputting the number of counts as the above-mentioned “predetermined time” is not limited to a specific time [second, minute], but is used for measurement. In consideration of the specifications of the equipment, for example, the fluctuation pitch assumed for the gas consumption and the required estimation accuracy are taken into consideration, and the time is appropriately set. Specifically, for example, as an example only, an appropriate value can be set in a range of about 5 to 60 seconds.

  In this embodiment, the above-mentioned “predetermined flow rate” is 1.2 [L] and the above-mentioned “predetermined time” is 10 seconds, that is, the actual flow rate 1.2 [L] is 1 Assume that measurement data in which a count number (referred to as “flow rate count number”) counted as a count is output at intervals of 10 seconds is acquired by the process of S1.

  Then, the measured value (specifically, the flow rate count) of the total gas consumption (main gas flow rate) obtained by the measurement is input to the gas consumption usage decomposing apparatus 10 via the data receiving unit 11a. The

  The measured value (flow rate count) of the total gas consumption may be input to the gas consumption usage decomposition apparatus 10 via a recording medium or a data server, or the gas consumption usage decomposition apparatus 10. And the measuring device may be electrically connected and input so that signals such as data and control commands can be transmitted and received (that is, input / output).

  When the measurement value is input via the recording medium, the flow rate count output from the measuring device is recorded on the recording medium, and the recording medium is connected to the recording medium connection terminal ( The flow rate count number may be input by being inserted into (not shown).

  When the measured value is input via the data server, the data server is connected to the gas consumption usage decomposition apparatus 10 through a signal line such as a bus, and the flow rate count output from the measuring device is the data count. It may be stored (saved) as a data file or the like on the server, and the flow rate count saved as the data file or the like may be read.

  When the gas consumption usage decomposition apparatus 10 and the measurement device are electrically connected and a measured value is input, for example, wired signal transmission / reception using cables connected to each other is used. It may be configured to be electrically connected so that signals can be transmitted / received through the above-described mechanism, or the signal can be transmitted through a wireless signal transmission / reception mechanism in which a wireless signal transceiver connected to each is used. You may make it electrically connect so that transmission / reception is possible. Then, the flow rate count number may be input by the signal transmission / reception mechanism.

  Furthermore, a flow rate count number may be input to the usage decomposition apparatus 10 for gas consumption by using a combination of a recording medium, a data server, and a wired / wireless signal transmission / reception mechanism.

  In addition, the time when each measured value (each flow count) of the total gas consumption is output from the measuring device is output together with the measured value by the clock function provided in the measuring device, and is associated with the measured value. The time when each measured value recorded or output from the measuring device is recorded or input to the recording medium, the data server, or the gas consumption usage decomposing apparatus 10 is associated with the measured value. It is recorded. The time interval [second, minute] is the above-described “predetermined time”.

  In the description here, the time recorded in association with each measured value (each flow count number) is called “measurement time”, and the combined data of the flow count number and measurement time is “measurement”. Called “data”.

  Then, the measurement data is stored in the memory 15 by the data receiving unit 11a.

  In addition, for example, the measurement data for one year over 24 hours related to the target household (in other words, in the main of the target house or the target dwelling unit) is a processing target for a group of gas consumption usage decomposition. Is stored in the memory 15.

  Next, the data of the time zone in which the application is decomposed is extracted from the measurement data measured and acquired by the process of S1 by the decomposition section extraction unit 11b of the control unit 11 (S2).

  The process of S2 is executed over the entire measurement data stored in the memory 15 in the process of S1 as the processing target of a group of gas consumption usage decomposition.

  Since the present invention decomposes the total gas consumption according to the application, the gas appliance is used in the target house / dwelling unit, and the gas consumption is actually used when the gas is actually consumed. The time zone when no gas is consumed is not treated.

  Therefore, based on the measurement data, the time period during which the total gas consumption may change continuously (specifically, increase) due to the use (in other words, operation) of any gas appliance (“ The measurement data of “continuous operation section” is cut out.

  Specifically, when the flow count number of the measurement data is 0 (zero) over the operation stop determination time [seconds, minutes], it is determined that the use of the gas appliance is interrupted. A time zone in which the flow count number of the measurement data is 0 over the operation stop determination time is referred to as an “operation stop section”.

  Then, a time zone sandwiched between two operation pause intervals preceding and following in time is cut out as a continuous operation interval. Therefore, it can be said that the continuous operation section is measurement data in a time zone in which the flow rate count number of the measurement data continuously changes without becoming 0 over the operation stop determination time.

  The operation pause determination time [seconds, minutes] is not limited to a specific value, and is appropriately set to an appropriate value in consideration of required estimation accuracy and the like. Specifically, for example, as an example only, a change in which the gas flow rate in the main is 0.2 [L / 10 seconds] or less will not cause a large error even if ignored as a minute flow rate (in other words, necessary) Is determined), the operation stoppage determination time is 10 seconds × 6 = 60 seconds based on 1.2L / 0.2L = 6 (that is, six measurement time intervals). It can be considered that

  Note that the value [seconds, minutes] of the operation stop determination time may be defined in advance in the usage decomposition program 17 for gas consumption, or via the input unit 13 before the processing of S2 is performed. It may be input by an operator.

  In the present embodiment, the measurement data stored in the memory 15 in the processing of S1 is sequentially read according to the time series by the decomposition section extraction unit 11b, and the operation stop section is specified based on the operation stop determination time. A time zone between two operation pause intervals before and after is extracted as a continuous operation interval.

  Then, the extracted measurement data for each continuous motion section is stored in the memory 15 as measurement data to be processed in subsequent S3 and S4 by the decomposition section extraction unit 11b.

  Next, the shortest broken line creation unit 11c of the control unit 11 creates a cumulative demand curve using the measurement data related to the continuous motion section extracted by the process of S2 (S3).

  The process of S3 is executed for each of the continuous motion sections extracted in the process of S2.

  The measurement data acquired by the process of S1 is the total gas consumption in increments of 1.2 L at intervals of 10 seconds. However, when any gas appliance is used, the occurrence of changes in the total gas consumption is continuous in time series in that the total gas consumption always increases, and in that the gas always flows. The mode of change in the total gas consumption is gradual.

  In other words, in the measurement in the processing of S1, in fact, the occurrence of the (value) change is continuous in the time series and the phenomenon in which the (value) change mode is gradual is a predetermined time interval and a predetermined value. It can be said that it is grasped as sporadic (in other words, intermittent) and discrete changes called ticks.

  In the present invention, the measurement data changes in a predetermined time interval and a predetermined flow rate increment are sporadic (in other words, intermittent) and discrete measurement data are not used as they are. (Intermittent) and discrete measurement data is used for processing of application decomposition after being converted into data in which the occurrence of a change in measurement value is continuous in time series and the aspect of the change in measurement value is gradual It is done.

  The occurrence of the change in the measured value is sporadic (intermittent) in time series and the change in the measured value is discrete, which is used in the application decomposition method, the application decomposition apparatus, and the application decomposition program of the gas consumption of the present invention. FIG. 3 shows a method for creating continuous data in which a cumulative demand curve is created in which the occurrence of changes in measurement values is continuous in time series and the mode of change in measurement values is gradual based on the data that is the target. Will be described.

  In addition, in the continuous operation section, the occurrence of changes in the total gas consumption is continuous over time in that the total gas consumption always increases as some gas appliance is used, and any gas appliance is used. Thus, the aspect of change in the total gas consumption is gradual in that the gas always flows.

  As an example of the explanation here, measurement data (specifically, a cumulative value of flow rate counts output every 10 seconds; Δ in the figure) as shown in FIG. Suppose that it was extracted by the process of S2. The actual flow rate per count is 1.2 [L].

  The horizontal axis in FIG. 3 represents each time point of the measurement time at intervals of 10 seconds, with the first measurement time of the continuous operation section being 1, and represented by a serial number as an identifier. That is, for example, “10” on the horizontal axis indicates that the time has elapsed by 100 seconds (= 10 × 10 seconds) from the start of the continuous operation section.

  Further, the vertical axis of FIG. 3 represents the cumulative value of the flow rate count number in the continuous operation section. That is, for example, “4” on the vertical axis indicates that gas is consumed by 4.8 [L] (= 4 × 1.2 [L]) from the beginning of the continuous operation section.

  The measurement data acquired by the processing of S1 is data that is sporadic (intermittent) and discrete with a change in value of a predetermined time interval and a predetermined flow rate, that is, occurrence of a change in the measurement value is time-series. 3 is a discrete (intermittent) and variation of measurement values is discrete data, and therefore, when the Δ marks that are data plots are connected in time series, a step shape is formed as shown in FIG. Reference numeral 21; referred to as "lower limit straight line 21").

  In addition, a data plot obtained by adding 1 for each measurement time to the accumulated value of the flow rate count of the measurement data and connecting the ▽ marks in time series are stepped as shown in FIG. 3 (in FIG. 3). (Referred to as “upper step stair straight line 22”).

  The lower limit step straight line 21 is the lower limit of the true accumulated value based on the actual total gas consumption (in other words, the lower limit of the cumulative value that the actual total gas consumption can take), and the upper limit step straight line 22 is the actual gas consumption. This is the upper limit of the true cumulative value based on the total consumption (in other words, the upper limit of the cumulative value that the actual total gas consumption can take).

  Therefore, a true cumulative demand curve representing the accumulation of actual total gas consumption in time series exists in an area (referred to as “existable area”) sandwiched between the lower limit stair straight line 21 and the upper limit stair straight line 22. As a whole, it is considered to be a non-decreasing curve showing an increasing trend.

  In addition, by analyzing the actual gas consumption characteristics of gas appliances generally used in the home, the number of large changes in the flow rate of gas consumed by the gas appliances is relatively small and almost constant. It was confirmed that the current flow rate tends to continue over a period of time.

  For this reason, the true cumulative demand curve has a relatively small number of break points (in other words, inflection points or flow velocity change points) among non-decreasing broken lines that show an increasing tendency in the existence possible region. Is considered highly relevant.

  Therefore, as a true cumulative demand curve representing the accumulation of actual total gas consumption in time series, the total length (that is, the length of the trajectory extending from the beginning to the end of the continuous operation section) existing in the existence possible region. ) Is estimated as the shortest broken line (referred to as “shortest broken line”).

  The estimation method of the shortest polyline that exists in the possible existence area as the true cumulative demand curve is not limited to a specific method, and exists in the specific area and reaches from the predetermined start point to the end point. There is an appropriate method for estimating a broken line whose total length among the broken lines is the shortest broken line and in which the total power demand measured at each measurement time and the total power demand estimated at each measurement time coincide. It is selected appropriately.

  As an estimation method of the shortest broken line, specifically, for example, as an example only, the allowable inclination of the straight line part (undefined) starting from the last point of the broken line that has been confirmed in the continuous motion section The upper and lower limit values are updated in sequence, and the slope of the straight line is determined when all the straight lines must deviate from the possible area, and the broken line up to the point when the straight line first touches the boundary of the possible area And a method based on the basic concept of repeating the same processing as described above after setting the last point of the newly determined broken line as a new starting point can be used.

In this estimation method, the measured flow rate at the measurement time t (the measurement unit is 30 minutes, and therefore the measurement time interval is 30 minutes) is set to x _t [L / 10 seconds] and the estimated flow velocity is set to y _t. Assuming that [L / 10 seconds], the optimization problem is expressed as Equation 4.

  In Expression 4, t is an identifier of the measurement time in the continuous operation section and t = 1, 2, 3,..., And in Expression 4, the measurement time identifier t is 1 to T as the range of the continuous operation section. Processing is performed over a period of time.

  By using the measurement time as an identifier in increments, the measurement time interval of 10 seconds is handled as one unit, and the increment from the measurement time t-1 to t is the increment per measurement time interval. It is the speed [L / 10 seconds] from the measurement time t-1 to t, and the true cumulative demand curve is the slope of the straight line from the measurement time t-1 to t.

In Expression 4, the measured flow rate x _t [L / 10 seconds] is the flow rate count number at the measurement time t (in other words, the increment of the flow rate count number from the measurement time t-1 to t) and the actual per count. The flow rate [L] is multiplied.

In Formula 4, f _u is an actual flow rate [L] per count (that is, f _u = 1.2 in this embodiment). Note that the value [L] of f _u may be defined in advance in the usage decomposition program 17 for gas consumption, or input by the operator via the input unit 13 before the processing of S3 is performed. You may be made to do.

  The first equation of the constraint condition corresponds to non-negative flow velocity, the second equation corresponds to the range limitation of the accumulated value of the actual total gas consumption, and the third equation is based on the measured value in the continuous operation section. This corresponds to the coincidence between the sum of the total gas consumption and the sum of the estimated total gas consumption.

  According to the above-described method for solving the optimization problem, even in the worst case, the shortest broken line corresponding to the true cumulative demand curve is estimated in the order of the square of the time series length (that is, the number of measurement times).

  As a result of applying the optimization problem expressed by Equation 4 to the lower limit stair straight line 21 connecting the measurement data plots (Δ in the figure) shown in FIG. 3, the actual gas shown by the dotted line in FIG. An estimated cumulative demand curve 23 that is considered to be close to the total consumption is obtained.

  Here, in the above optimization problem, when the process is executed from the measurement time identifier t = 1, in order to execute the process of t = 1, t = 0 as the starting point at t = 1 (that is, It is necessary to specify the last point of the polygonal line from t = 1 to the measurement time immediately before the measurement time corresponding to the measurement time identifier t = 1, that is, the straight line from t = 0 to t = 1 Need to be identified. Specifically, this straight line sets the identifier t to 0 (zero) as the measurement time immediately before the first measurement time (that is, the measurement time identifier t = 1) in the continuous operation section and the flow count number. It is specified by placing a virtual origin where the accumulated value is 0 (zero) and connecting the origin to the value of the accumulated value of the flow rate count at the measurement time identifier t = 1.

  Alternatively, based on the characteristics of gas consumption in the home that a nearly constant flow rate tends to continue for a certain period of time, a true cumulative demand curve with a small number of break points is appropriate. Based on the idea that it is high, the slope of the straight line from the identifier t = 0 to t = 1 of the measurement time for the continuous motion section may be matched with the slope of the continuous straight line at the identifier t = 1. good.

  Here, the above-described estimation method of the true cumulative demand curve (that is, the optimization problem expressed by Expression 4) is obtained by changing the objective function part from “sum of distances” to “sum of squares of distances”. The problem of obtaining the optimization problem represented by the above, that is, the “curve with the smallest sum of squares of the flow velocity at each measurement time (that is, the difference between the measurement times of the accumulated value of the flow rate) among the arbitrary curves in the existence region” Is equivalent.

  In the optimization problem expressed as Equation 5, since the flow velocity at each measurement time tends to have the same value due to the minimization of the sum of squares, the broken line becomes the optimal solution.

The optimization problem expressed by Formula 4 as a method for estimating the true cumulative demand curve is also equivalent to the optimization problem expressed by Formula 6 that is a dual problem of the optimization problem expressed by Formula 5. . Note that the optimization problem expressed by Equation 6 is a kind of convex optimization problem called a total variation normalization problem. In Equation 6, x ₀ is an initial value of the measured flow rate x _t [L / 10 seconds] (that is, a value when t = 0), and in the example shown in FIG. 3, x ₀ = 0.

  The above-mentioned true cumulative demand curve (that is, the shortest broken line) can be obtained by using the following algorithm as an example.

-Algorithm for determining the true cumulative demand curve-
<Retention / update information>
○ Starting line Line ○ Upper limit line Upper limit demand and area boundary contact point ○ Lower limit line Lower limit demand and area boundary contact point
(1) Time update: Time = Time + 1
(2) Lower limit straight line <Upper limit accumulated demand value Demand from the starting point to the lower boundary contact point = Demand lower limit (confirmed)
Starting point = Boundary contact point of the lower limit straight line Time = Starting point, upper limit / lower limit straight line reset
Go to (1)
(3) Upper limit straight line> Lower limit cumulative demand Demand from the starting point to the upper boundary contact point = demand upper limit (confirmed)
Starting point = boundary contact point of upper limit straight line Time = resetting of starting point and upper / lower limit straight line
Go to (1)
(4) Lower limit straight line <Region lower limit Lower limit demand, lower limit boundary setting update Upper limit straight line> Region upper limit Upper limit demand, upper limit boundary setting update
(5) Return to (1) until the measurement time identifier t = T.

  In the present embodiment, the shortest broken line creation unit 11c reads the measurement data related to the continuous operation section stored in the memory 15 in the process of S2 (that is, the combination data of the flow rate count number and the measurement time).

  The flow rate count read from the memory 15 is multiplied by 1.2 [L], which is the actual flow rate per count, and converted to an actual gas flow rate [L], and the measurement time is the continuous time. .. Are converted into sequential numbers of 1, 2, 3,...

Subsequently, the shortest broken line creation unit 11c uses the converted measurement data to solve any of the optimization problems expressed by Equations 4 to 6, and the gas for each identifier t at the measurement time. The estimated flow velocity y _t [L / 10 seconds] is calculated.

Then, the value of the estimated estimated gas flow velocity y _t [L / 10 seconds] is associated with the value of the identifier t of the measurement time for each continuous operation section extracted in the process of S2 by the shortest broken line creation unit 11c. (in other words, as the combination data and the value of the estimated velocity y _t values of identifiers t and gas measurement time) it is to be stored in the memory 15.

  Next, the use gas decomposition unit 11d of the control unit 11 uses the gas flow rate for each measurement time calculated by the process of S3 to decompose the total gas consumption into the gas usage for each application (S4). ).

  The process of S4 is executed for each of the continuous operation sections extracted in the process of S2 and for which the gas flow rate for each measurement time is calculated in the process of S3.

Specifically, the application decomposition unit 11d, the identifier value t of the measurement time on Continuous operation section stored in the memory 15 in the processing of S3 (t = 1,2, ..., T) and the gas estimation of velocity y _t Combination data with the value of is read.

  Subsequently, in the present embodiment, the total gas consumption is decomposed into the gas usage for each application according to the algorithm shown in FIG.

  Specifically, first, it is determined whether or not the continuous operation section is in winter (S4-1).

  The method of determining whether or not it is the winter season is not limited to a specific criterion, and there is a difference between the winter season and other seasons such as the presence or absence of use of a heater or the temperature setting of a hot water heater (or the like). It is set appropriately in an appropriate method after taking into account the time and the like. Specifically, for example, as an example only, a method of determining that the data of the continuous operation section extracted from the measurement data acquired in the period from October to April of the following year is the winter season may be employed.

  And when the said continuous operation area is a thing in winter (S4-1: Yes), it progresses to the process of S4-2.

Then, the estimated velocity y _t is equal to or less than the maximum value Vhs-max heating reference velocity with which the minimum value Vhs-min or more heating reference flow rate, and the condition is satisfied velocity duration ( "predetermined flow rate Duration" It is determined whether it is equal to or longer than the heating operation determination time Th (S4-2).

  In the determination process of S4-2, although the flow velocity is not necessarily large, if the duration time of such a flow velocity is long, it is considered that at least the heating gas appliance is used, and the heating gas appliance operates continuously. It is a process for specifying the time zone (referred to as “heating continuous operation section”).

  The minimum value Vhs-min and the maximum value Vhs-max [L / 10 seconds] of the heating reference flow velocity are based on the minimum value and the maximum value of the range assumed as the gas consumption when the heating gas appliance is used. It is not limited to a specific value. For example, the specifications of the gas appliance for heating installed in the target house / unit (or assumed to be installed in general) are considered. Or when actual measurement data is collected, the actual measurement data is taken into consideration, and an appropriate value is set. Specifically, for example, as an example only, the minimum value Vhs-min is an appropriate value in the range of about 0.01 to 0.2 [L / 10 seconds], and the maximum value Vhs-max is 0.5 to Each can be set to an appropriate value in the range of about 1.5 [L / 10 seconds].

  Further, the heating operation determination time Th [seconds, minutes] is not limited to a specific value, and is set to an appropriate value in consideration of, for example, a time that is assumed as the minimum duration time when the heater is used. Set as appropriate. Specifically, for example, it can be set to an appropriate value in a range of about 5 to 15 minutes, as an example.

  The process of S4-2 is executed over the entire continuous operation section. And as a result of the process of S4-2, about each continuous operation area, one heating continuous operation area is not specified, or one or more heating continuous operation areas are specified.

If Vhs-min ≦ y _t ≦ Vhs-max and the predetermined flow rate duration is equal to or greater than Th [minutes] (S4-2: Yes), the process proceeds to S4-3. In other words, when one or more heating continuous operation sections are specified in the process of S4-2, the process proceeds to the process of S4-3.

  Then, the heating base flow velocity Vhb is set (S4-3).

The heating base flow velocity Vhb [L / 10 seconds] is set to the minimum value of the estimated flow velocity y _t [L / 10 seconds] in all the heating continuous operation intervals specified in the continuous operation interval.

  Therefore, the heating base flow velocity Vhb is set as a value that is commonly used for a certain continuous operation section, and the value may be different in different continuous operation sections.

Then, the estimated velocity y _t is equal to or greater than or equal to the sum of the heating base flow rate Vhb and hot water minimum flow rate Vw is determined (S4-4).

  The process of S4-4 is performed about each measurement time t of each heating continuous operation area specified in the process of S4-2.

  The determination process of S4-4 is performed for each measurement time t in the time zone specified as a heating continuous operation section in the continuous operation section, that is, in a state where the heating gas appliance is used. This is a process for determining whether or not an instrument is used.

  The hot water supply minimum flow velocity Vw [L / 10 seconds] is set based on the minimum value of the range assumed as the gas consumption when the hot water supply gas appliance is used, and is not limited to a specific value. For example, when the specifications of gas appliances for hot water supply installed (or assumed to be installed) in the target house / unit are taken into account, or when measured data is collected Is appropriately set to an appropriate value in consideration of the measured data. Specifically, for example, as an example only, an appropriate value can be set in a range of about 0.5 to 1.5 [L / 10 seconds].

Then, for each measurement time t in the heating the continuous operation period of the continuous operation period, if a _{y t ≧ Vhb + Vw (S4-4} : Yes) from the estimated velocity y _t minus the heating base flow rate Vhb Heating base flow rate Vhb with are classified into gas consumption of the hot water supply amount is classified in gas consumption of the heating amount, and when not, _{y t ≧ Vhb + Vw (S4-4} : no) estimated velocity y _t is the heating component gas Classified into consumption.

  On the other hand, in the process of S4-1, when the continuous operation section is not in the winter season (S4-1: No), the process proceeds to S4-5. And the process of S4-5 thru | or S4-7 is performed about each of the measurement time t.

Further, in the process of S4-2, when Vhs-min ≦ y _t ≦ Vhs-max is not satisfied, or when the predetermined flow velocity duration is not equal to or more than Th [min] (S4-2: No), the process of S4-5 is performed. Proceed to In other words, the processes of S4-5 to S4-7 are executed for each measurement time t that is not a heating continuous operation section.

First, the estimated velocity y _t is is determined whether or not the hot water minimum flow rate Vw or (S4-5).

  The determination process of S4-5 is a process for determining whether or not the hot water supply gas appliance is used.

Then, if a _{y t ≧ Vw (S4-5: Yes} ) estimated velocity y _t are classified into gas consumption of the hot water supply amount.

On the other hand, y _t ≧ If not Vw is (S4-5: No) the process proceeds to S4-6 processing, the continuous operation period is whether or not in summer is determined.

  The method of determining whether or not it is the summer season (S4-6) is not limited to a specific criterion. For example, in the summer and other seasons, for example, whether or not the heater is used and the temperature setting of the water heater is set. An appropriate method is set in consideration of the time when the difference appears (or is considered to appear). Specifically, for example, as an example only, a method of determining that the data of the continuous motion section extracted from the measurement data acquired in the period from November to March of the following year is the summer season may be employed.

  In addition, when judging whether or not it is winter or summer, the period designated as winter and summer may be continuous without interruption, and it may be continuous in winter and summer. There may be a period that is not specified (classified), or a part of the period specified as winter and the period specified as summer may overlap.

When the continuous operation interval is in the summer (S4-6: Yes) estimated velocity y _t are classified into gas consumption of kitchen minute.

On the other hand, the continuous operation interval when not intended in summer: advances to the processing of (S4-6 No) S4-7, the estimated flow rate y _t whether greater than kitchen minimum velocity Vk is determined.

  The determination process of S4-7 is a process for determining whether or not the kitchen gas appliance is used.

  The kitchen minimum flow velocity Vk [L / 10 seconds] is set based on the minimum value of the range assumed as the gas consumption when the kitchen gas appliance is used, and is not limited to a specific value. For example, when the specifications of gas appliances for kitchens installed (or assumed to be installed) in the target house / unit are taken into account, or when measured data is collected Is appropriately set to an appropriate value in consideration of the measured data. Specifically, for example, as an example only, an appropriate value can be set in a range of about 0.2 to 1.0 [L / 10 seconds].

  Here, in the process of S4-7 of the present embodiment, a state where only the kitchen gas appliance is used is distinguished from a state where the heating gas appliance operates intermittently for heat insulation. ing.

  Therefore, the value of the kitchen minimum flow velocity Vk is the maximum value (referred to as “intermittent heating maximum flow velocity”) that is assumed as the amount of gas consumed when the heating gas appliance is operating intermittently to keep warm. It may be considered together or set based on the maximum intermittent heating flow velocity.

Then, when the estimated velocity y _t is larger than the kitchen minimum flow rate Vk is (S4-7: Yes) estimated velocity y _t are classified into gas consumption of kitchen minute.

On the other hand, the estimated flow rate y _t is the case not greater than the kitchen minimum velocity Vk (S4-7: No) estimated velocity y _t are classified into gas consumption of the heating component.

  And the integrated value [L] in units of time, day, week, month, season, and / or year, such as 30 minutes or 1 hour, for example, of the gas usage amount for each gas appliance is calculated by the application decomposition unit 11d. These are recorded in a data file or the like and stored (saved) in the storage unit 12 or displayed on the display unit 14.

  It should be noted that various values such as threshold values used in the process of S4 may be defined in advance in the usage decomposition program 17 of the gas consumption, or the input unit 13 is set before the process of S4 is performed. May be input by the operator.

  And the control part 11 complete | finishes the process regarding measurement data memorize | stored in the memory 15 in the process of S1 as a process target of a group of use decomposition | disassembly of a gas consumption (END).

According to the application decomposition method, the application decomposition apparatus, and the application decomposition program of the gas consumption configured as described above, the measurement time at a predetermined time interval and the value output at each measurement time, the predetermined flow rate Combination data with the flow rate count counted as 1 count for every f _u [L], that is, a change in a value of a predetermined time interval and a predetermined flow rate f _u is sporadic (in other words, intermittent) and Based on data that is discrete, or in other words, data that occurs sporadically (intermittently) in a time series, and the manner in which the value changes are discrete, the gas flow rate y at each measurement time _{Since t} can be calculated, it becomes possible to improve the reproducibility of the actual state of total gas consumption.

Configured application method for decomposing gas consumption as described above, applications cracker, and according to the application decomposer, further realities of total consumption of gas on the basis of the flow velocity y _t good reproduced gas The usage can be classified into hot water, heating, and kitchen, so that the accuracy of the usage decomposition of the total gas consumption is ensured and the reliability of the gas consumption as a usage decomposition technology is improved. It becomes possible.

  Although the above-described embodiment is an example of a preferred embodiment for carrying out the present invention, the embodiment of the present invention is not limited to the above-described embodiment, and the present invention is not deviated from the gist of the present invention. Various modifications can be made.

For example, in the above-described embodiment, the algorithm shown in FIG. 4 is used when the total gas consumption is decomposed into the gas usage for each application in the process of S4. It is not restricted to what is shown in 4. Specifically, the main points of the application decomposition of the total gas consumption in the present invention are:
1) Process for determining whether or not the continuous operation section is in winter (in the above-described embodiment, the process of S4-1 is applicable).
2) Processing for specifying a heating continuous operation section based on whether or not the state in which the gas flow velocity (y _t ) in the trunk is in a predetermined range continues for a predetermined time (Th) (in the above embodiment, (S4-2 is applicable)
3) A process in which the minimum value of the gas flow velocity (y _t ) in all the heating continuous operation intervals specified in the continuous operation interval is set as the heating base flow velocity (Vhb) (S4 in the above embodiment) -3 is applicable)
4) Processing in which the sum of the heating base flow velocity (Vhb) and the minimum hot water supply flow velocity (Vw) is taken into consideration (in the above-described embodiment, the processing of S4-4 corresponds)
5) Considering at least one of the processing considering the minimum hot water supply flow velocity (Vw) (the processing of S4-5 corresponds to the above-described embodiment) and the minimum flow velocity of kitchen (Vk) and the maximum intermittent heating flow velocity. Process to be performed (in the above embodiment, the process of S4-7 corresponds)
Is included.

  The order in which the processes 1 to 4 are executed is not limited to that shown in FIG.

  Further, as can be seen from the list of the main points listed as 1 to 4, the process for determining whether or not the continuous operation section is in the summer season (in the above embodiment, the process of S4-6 corresponds). Is not an essential process in the present invention. For example, by adjusting the period designated as winter in the case of determining whether the continuous operation section is winter in the process of S4-1 as in the above-described embodiment, The process for determining whether or not the continuous operation section is in the summer may be excluded.

  Each threshold value used in each process is not limited to a specific value, and can be set as appropriate.

  Next, FIG. 5 to FIG. 10 show examples of embodiments of the determination method, determination device, and determination program for the resident absence from home according to the present invention.

  Here, the determination of absence from home in the present invention is either a state where a resident is present in a residence or a dwelling unit (that is, is home) or a state where a resident is not present (that is, is absent). It is to estimate whether there is.

  Also in the determination of the absence of the resident at home in the present invention, the concept of the continuous data creation method shown in FIG. 3 is used. Then, the actual power demand is estimated from the measurement values output in a predetermined time interval and in a predetermined unit of measurement, and the occupant's absence from home is determined using the estimated power demand.

Specifically, the determination method of the absence of the resident at home in the present embodiment is a measurement time at a predetermined time interval and a value output at each measurement time, and measures a predetermined power amount l as a measurement unit. Combination data for a predetermined time period is extracted as data of the analysis target section from the measurement data of the power demand acquired as the combination data with the power demand (S1), and the power demand for each measurement time in the analysis target section. The total length from the start point to the end point of the analysis target section exists in a region having a lower limit of the cumulative value and a cumulative value of the power demand amount at each measurement time plus a predetermined power amount l. A broken line that is the shortest and has the same total power demand amount measured at each measurement time and the estimated total power demand amount at each measurement time is estimated (S2). Is calculated power demand rate x _t is the slope of the line (S2), the power demand in the predetermined time period used power demand rate x _t included in a predetermined period of measurement time before the subject of the determination of the home absent statistics velocity x _t is calculated as a feature amount, and at least on the feature amount home absent residents used is determined (S3) as (see FIG. 5).

  In addition, the resident absence-at-home determination device according to the present embodiment includes a measurement time at a predetermined time interval and a value output at each measurement time, and a power demand amount that is measured using a predetermined power amount l as a measurement unit. The data receiving unit 31a as a means for receiving the measurement data of the power demand acquired as the combination data, and the combination data of the predetermined time zone is extracted from the measurement data as the analysis target section data, and the analysis target An analysis target section that exists in an area whose upper limit is a value obtained by adding a predetermined power amount l to a cumulative value of the power demand amount at each measurement time and a cumulative value of the power demand amount at each measurement time in the section. Estimate a line that has the shortest total length from the start point to the end point, and that matches the total power demand measured at each measurement time with the total power demand estimated at each measurement time. The shortest broken line creation unit 31b as a means for calculating the power demand rate as the slope of the straight line between each measurement time in the broken line, and the power demand rate included in a predetermined period before the measurement time that is the target of absence from home. And a time-specific determination unit 31c as means for determining a statistical amount of the power demand rate during the predetermined period as a feature amount and determining at least the presence of the resident by using the feature amount.

  The resident absence-at-home determination method and the resident vacancy determination device can be implemented and realized by executing a resident absence-at-home determination program on a computer. Here, a case will be described in which a resident resident absence determination method is implemented and a resident resident absence determination device is implemented by executing a resident resident absence determination program on a computer.

  FIG. 7 shows an overall configuration of a computer 30 (in this embodiment, which is also a determination device 30 for determining whether a resident is away from home) for executing the resident absence-at-home determination program 37 according to the present embodiment.

  The computer 30 (the resident absence determination device 30) includes a control unit 31, a storage unit 32, an input unit 33, a display unit 34, and a memory 35, which are connected to each other by a signal line such as a bus. Yes.

  The control unit 31 performs a calculation related to the control of the entire computer 30 and the determination of the absence of the resident at home according to the resident absence-at-home determination program 37 stored in the storage unit 32. Processing device).

  The storage unit 32 is a device capable of storing at least data and programs, and is, for example, a hard disk.

  The input unit 33 is an interface (that is, an information input mechanism) for giving at least an operator's command and various information to the control unit 31, and is, for example, a keyboard or a mouse. For example, a plurality of types of interfaces such as a keyboard and a mouse may be provided as the input unit 33.

  The display unit 34 performs drawing / display of characters, figures, images, and the like under the control of the control unit 31 and is, for example, a display.

  The memory 35 is a memory space that is a work area when the control unit 31 executes various controls and calculations, and is, for example, a RAM (abbreviation of Random Access Memory).

Then, the control unit 31 of the computer 30 (hereinafter referred to as “a resident's absence determination device 30”) executes a determination program 37 of the resident's absence at home, so that a predetermined time interval is obtained. A process of receiving measurement data of power demand obtained as combination data of a measurement time and a power demand that is a value output at each measurement time and measures a predetermined power amount l as a measurement unit is performed. The combination data of the predetermined time zone is extracted from the data receiving unit 31a and the measurement data as the data of the analysis target section, and the cumulative value of the power demand for each measurement time in the analysis target section is set as the lower limit and for each measurement time The total length from the start point to the end point of the analysis target section is the shortest, and exists in an area whose upper limit is a value obtained by adding a predetermined power amount l to the accumulated power demand amount of Each measurement time of the power demand of the power demand rate as the slope of the straight line between each measurement time by estimating a line the sum and matches at the fold line x _t which is estimated the sum of the power demand amount measured in the measuring time Statistics of the power demand speed x _t in a predetermined period using the shortest broken line creation unit 31b that performs the process of calculating the power demand speed and the power demand speed x _t included in the predetermined period before the measurement time that is the target of absence from home A time-based determination unit 31c that calculates the amount as a feature amount and performs a process of determining at least the presence of the resident by using the feature amount is configured.

  Here, as processing in the computer for the variables used when performing the processing described below, first, all the variables representing the absent start time, absent end time, and absent interval index are initialized. Specifically, For example, a numerical value that cannot be given as a time is given to a variable that represents the absence start time and the absence end time, and 0 (zero) is given to a variable that represents the absence section index, for example.

  And as implementation of the determination method of a resident's absence at home, power demand is first measured and measurement data is acquired (S1).

  The method for measuring the power demand is, for example, measuring the power demand in a house or dwelling unit that is the target of absence from home and measuring, for example, electricity every predetermined time in a predetermined unit of measurement (in other words, a predetermined unit of measurement). As long as the demand amount in the predetermined unit of measurement is output as a general signal, it is not limited to a specific method or device, and a method or device capable of performing the above measurement is appropriately selected. Is done.

  Specifically, for example, as an example only, a smart meter (for example, Non-Patent Document 2 described above) may be used as a method of measuring the power demand (specifically, a measuring device).

  The actual power amount treated as one unit (in other words, measured as one unit) as the above-mentioned “predetermined measurement unit” in the measurement of power demand is limited to a specific value [Wh]. Instead, for example, it is appropriately set to an appropriate value in consideration of the specifications of the measuring device used for measurement. Specifically, for example, in the A route measurement value of the smart meter, the actual amount of power handled as one unit is 100 Wh.

  In addition, the time interval for outputting the measurement value as the above-mentioned “predetermined time” in the measurement of the electric power demand is not limited to a specific time [minute]. For example, the specification of the measuring device used for the measurement Based on the above, it is set appropriately at an appropriate time. Specifically, for example, in the A route measurement value of the smart meter, the time interval at which the measurement value is output is 30 minutes.

  In the present embodiment, a smart meter is used in the measurement of power demand, and the above-mentioned “predetermined measurement unit” is 100 Wh and the above-mentioned “predetermined time” is 30 minutes, that is, actual data It is assumed that measurement data in which a value of power demand (referred to as “demand unit measurement value”) measured with the amount of power 100 Wh as one unit is output by the process of S1 is obtained at intervals of 30 minutes.

  And the measured value (specifically demand unit metric value) of the electric power demand acquired by measurement is inputted into judgment device 30 of a resident's absence from home via data receiving part 31a of control part 31. The

  The measurement value (demand unit measurement value) of the electric power demand may be input to the resident absence-at-home determination device 30 via various recording media or a data server, or like a smart meter, for example. It may be inputted by a communication function provided in the measuring device or a communication function or a communication mechanism added to the measuring device.

  When the measurement value is input via the recording medium, for example, the demand unit measurement value output from the measuring device is recorded on the recording medium, and the recording medium is a recording medium of the resident absence-at-home determination device 30. It is conceivable that the demand unit measurement value is input by being inserted into a connection terminal (not shown).

  When the measurement value is input via the data server, for example, the data server is connected to the occupant's absence determination device 30 through a signal line such as a bus, and the demand unit metric output from the measurement device It is conceivable that the value is stored (saved) as a data file or the like in the data server, and the demand unit metric value saved as the data file or the like is read.

  Further, for example, when a measurement value is input by a communication function or a communication mechanism added to the measurement device such as a smart meter, the demand unit measurement value is real-time (that is, It can also be input to the occupant's absence-at-home determination device 30 (immediately), and therefore it is possible to process the determination of the resident's absence at home in almost real time (ie, immediately).

  Note that the demand unit metric value may be input to the occupant's absence determination device 30 using a combination of a recording medium, a data server, and a communication function and a communication mechanism.

  In addition, the time when each measured value of electricity demand (measured value of each demand unit) is output from the measuring device is output together with the measured value by the clock function provided in the measuring device and is associated with the measured value. The time when each measured value recorded or outputted from the measuring device is recorded or input to the recording medium, the data server, or the resident absence-at-home determination device 30 is associated with the measured value. Or recorded. The time interval [minute] is the above-mentioned “predetermined time”.

  In the explanation here, the time recorded in association with each measured value (each demand unit metric value) is called “measurement time”, and the combination data of the demand unit metric value and the measurement time is This is called “measurement data”.

  Then, the measurement data is stored in the memory 35 by the data receiving unit 31a.

  Next, the accumulated demand curve is created by the shortest broken line creating unit 31b of the control unit 31 using the measurement data measured and acquired by the process of S1 (S2).

  The process of S2 is executed for the analysis target section. The analysis target section is set as a period including at least a time (referred to as “determination target time”) at which a determination is made that the resident is away from home for the target house or the target dwelling unit.

  The analysis target section is not limited to a specific time length [time]. For example, an appropriate time length is considered by taking into account the time assumed for the absence period assumed as the main target of detection. Is set as appropriate. Specifically, the analysis target section can be set to an appropriate time length in a range of about 6 to 36 hours, for example, as an example only. The analysis target section is based on the determination target time as the determination target time progresses, for example, when a determination is made that the resident is away from home in real time (that is, immediately). For example, it may be updated in a timely and appropriate manner (in other words, reset) and moved in the time axis direction.

  The determination target time may be set such that all the measurement times are sequentially set as the determination target times, or the measurement times selected at predetermined intervals from the respective measurement times are sequentially determined. You may make it be time.

  The measurement data acquired by the process of S1 is a power demand amount in units of 100 Wh (in other words, every 100 Wh) at 30-minute intervals. However, when some kind of electrical equipment is used, it is usually used in ordinary houses, such as refrigerators, which are always in operation and whose power demand fluctuates at a predetermined interval (or a predetermined trigger). In view of the fact that demand for power (including standby power) is constantly maintained, changes in power demand are continuous in time series, and power is always consumed. In FIG. 4, the change in the power demand is gradual.

  In other words, in the measurement in the processing of S1, in fact, the occurrence of the (value) change is continuous in the time series and the phenomenon in which the (value) change mode is gradual is a predetermined time interval and a predetermined value. It can be said that it is grasped as a sporadic (in other words, intermittent) and discrete change of units (in other words, predetermined value increments, predetermined measurement units, or predetermined measurement units).

  In the present invention, the change in the measurement value is sporadically (in other words, intermittent) in a predetermined time interval and in a predetermined value unit (in other words, a predetermined value unit, a predetermined measurement unit, or a predetermined measurement unit). In addition, the measurement data that is discrete and is not used as it is, but the sporadic (intermittent) and discrete measurement data is that the occurrence of the change in the measurement value is continuous in time series and the change in the measurement value. Are converted into data that is gradually changed, and used for the determination of absence from home.

  The occurrence of changes in measurement values is sporadic (intermittent) in a time series and the change in measurement values is discrete, which is used in the determination method, determination apparatus, and determination program for resident absence from home according to the present invention. FIG. 8 shows a method for creating continuous data in which a cumulative demand curve is created in which the occurrence of changes in measurement values is continuous in time series and the mode of change in measurement values is gradual, based on the data of FIG. It explains using. The concept of the creation method (in other words, the main point) is the same as the concept (main point) of the creation method described with reference to FIG.

  As an example of the explanation here, as shown in FIG. 8, measurement data in a certain time zone, specifically, a demand unit measurement value that is output every 30 minutes, for example, A route measurement value of a smart meter. And a cumulative value of power demand in units of 100 Wh (marked with ● in the figure) is acquired by the processing of S1 and extracted as measurement data for the analysis target section.

  The horizontal axis of FIG. 8 represents each time point of the measurement time which is an interval of 30 minutes, with the first measurement time of the analysis target section being 1, and represented by a serial number as an identifier. That is, for example, “3” on the horizontal axis indicates that 1.5 hours (= 90 minutes = 30 minutes × 3) have elapsed since the beginning of the analysis target section.

  Moreover, the vertical axis | shaft of FIG. 3 represents the accumulation value of the demand unit measured value in the said analysis object area. That is, for example, “400” on the vertical axis indicates that power is consumed by 400 [Wh] from the beginning of the analysis target section.

  The measurement data acquired by the process of S1 is a discrete (intermittent) and discrete data in which a change in value of a predetermined time interval and a predetermined value unit (value increment, measurement unit, measurement unit) is obtained. Since the occurrence of changes in measurement values is sporadic (intermittent) in the time series and the variation of measurement values is discrete data, it is a data plot of accumulated values of demand unit measurement values of measurement data Are connected in time series to form an irregular ascending staircase shape as shown in FIG. 8 (reference numeral 41 in FIG. 8; referred to as “lower limit stair straight line 41”).

  In addition, a ▲ mark is a plot of data obtained by adding 100 Wh (that is, the unit of measure in the demand unit measurement value and the size of the value increment) to the accumulated value of the measurement unit demand value of the measurement data at each measurement time. Those connected in time series also have an irregular ascending staircase shape as shown in FIG. 8 (reference numeral 42 in FIG. 8; referred to as “upper staircase straight line 42”).

  The lower limit stair straight line 41 is the lower limit of the true cumulative value based on the actual power demand (in other words, the lower limit of the cumulative value that the actual power demand can take), and the upper limit stair straight line 42 is the actual power demand. The upper limit of the true cumulative value based on (in other words, the upper limit of the cumulative value that the actual power demand can take).

  Therefore, a true cumulative demand curve representing the accumulation of actual power demand in a time series exists in an area (referred to as “existable area”) sandwiched between the lower limit stair straight line 41 and the upper limit stair straight line 42. As a whole, it is considered to be a non-decreasing curve showing an increasing trend.

  And as a true accumulated demand curve, the number of break points (in other words, inflection points or power demand speed change points) is relatively small among non-decreasing broken lines that show an increasing tendency in the existence possible region. Assume something.

  Therefore, the total length (ie, the length of the trajectory extending from the beginning to the end of the analysis target section) existing in the possible area as a true cumulative demand curve representing the accumulation of actual power demand in time series. Estimates the shortest line (referred to as “shortest line”).

  The estimation method of the shortest polyline that exists in the possible existence area as the true cumulative demand curve is not limited to a specific method, and exists in the specific area and reaches from the predetermined start point to the end point. There is an appropriate method for estimating a broken line whose total length among the broken lines is the shortest broken line and in which the total power demand measured at each measurement time and the total power demand estimated at each measurement time coincide. It is selected appropriately.

  As an estimation method of the shortest broken line, specifically, for example, as an example only, the allowable slope of the straight line portion (undefined) starting from the last point of the broken line that has been confirmed in the analysis target section The upper and lower limit values are updated in sequence, and the slope of the straight line is determined when all the straight lines must deviate from the possible area, and the broken line up to the point when the straight line first touches the boundary of the possible area And a method based on the basic concept of repeating the same processing as described above after setting the last point of the newly determined broken line as a new starting point can be used.

This estimation method sets the measured power amount at measurement time t (where the measurement unit is 30 minutes, and therefore the measurement time interval is 30 minutes) to x _t [Wh / 30 minutes], and the estimated power Assuming that the demand rate is y _t [Wh / 30 minutes] (see FIG. 9), the optimization problem is expressed as Equation 7.

  In Expression 7, t is an identifier of the measurement time in the analysis target section and t = 1, 2, 3,..., And in Expression 7, the measurement time identifier t is 1 to T as the range of the analysis target section. Processing is performed over a period of time.

  By setting the measurement time as an identifier in increments, the measurement time interval of 30 minutes is treated as one unit, and the increment from the measurement time t-1 to t is the increment per measurement time interval. It is the speed [Wh / 30 minutes] from the measurement time t-1 to t, and the true cumulative demand curve is the slope of the straight line from the measurement time t-1 to t.

In Equation 7, the measured electric energy x _t [Wh / 30 minutes] is a demand unit metric value at the measurement time t (in other words, an increase in the demand unit metric value from the measurement time t−1 to t 1).

  In Expression 7, l (el) is a minimum unit (that is, a measurement unit and a measurement unit) of the power demand [Wh], and in this embodiment, l = 100.

The minimization of the object in Equation 7, the elapsed time (i.e., each 1) and the estimated demand for each elapsed time (i.e., the estimated power demand speed y _t) is the distance of a polygonal line consisting of a. Distance polygonal line, as in the example shown in FIG. 9, the elapsed time (i.e., each 1) obtained as the sum of the length of the hypotenuse against the estimated power demand rate y _t related to the time elapsed, the hypotenuse in the present invention The curve with the smallest sum of the lengths is the estimated cumulative demand curve.

  The first equation of the constraint condition corresponds to the non-negative power demand rate, the second equation corresponds to the range constraint of the accumulated value of the actual power demand, and the third equation corresponds to the analysis target section. Corresponds to the coincidence of the sum of the measured value-based power demand and the sum of the estimated value-based power demand.

  According to the above method for solving the optimization problem, even in the worst case, the shortest broken line corresponding to the true cumulative demand curve is estimated in the order of the square of the time series length (that is, the number of measurement times).

  As a result of applying the optimization problem expressed by Equation 7 to the lower limit stair straight line 41 connecting the plots of measurement data shown in FIG. 8 (● marks in the drawing) (in other words, the three constraints shown in Equation 7) The entire accumulated demand curve that satisfies the condition) is obtained as an estimated accumulated demand curve 43 that is considered to be close to the actual power demand, as a monotonically increasing curve in the possible existence area indicated by a broken line in FIG.

  Here, in the above optimization problem, when the process is executed from the measurement time identifier t = 1, in order to execute the process of t = 1, t = 0 as the starting point at t = 1 (that is, It is necessary to specify the last point of the broken line from t = 1 to the measurement time immediately before the measurement time corresponding to the measurement time identifier t = 1, that is, the straight line from t = 0 to t = 1 Need to be identified. Specifically, this straight line has the identifier t as 0 (zero) as the measurement time immediately before the first measurement time (that is, the measurement time identifier t = 1) in the analysis target section and the demand unit measurement value. The virtual origin with the accumulated value of 0 is set to 0 (zero), and the origin is specified by connecting the point of the accumulated value of the demand unit metric value at the measurement time identifier t = 1.

  Alternatively, the slope of the straight line from the measurement time identifier t = 0 to t = 1 for the analysis target section may be matched with the slope of the continuous line at the identifier t = 1.

  Here, the above-described estimation method of the true cumulative demand curve (that is, the optimization problem expressed by Equation 7) is obtained by changing the objective function portion from “sum of distances” to “sum of squares of distance”. In other words, an optimization problem expressed by the following: “a curve having the smallest sum of squares of the power demand rate at each measurement time (that is, the difference between measurement times of the accumulated value of the power demand amount) in an arbitrary curve of the existence possible region Is equivalent to the problem of

  In the optimization problem expressed by Equation 8, since the power demand speed (power demand amount) at each measurement time tends to have the same value due to the minimization of the sum of squares, the broken line becomes the optimal solution.

The optimization problem expressed by Expression 7 as a method for estimating the true cumulative demand curve is also equivalent to the optimization problem expressed by Expression 9 that is a dual problem of the optimization problem expressed by Expression 8. . Note that the optimization problem expressed by Equation 9 is a kind of convex optimization problem called a total variation normalization problem. In Equation 9, x ₀ is an initial value of the measured electric energy x _t [Wh / 30 minutes] (that is, a value when t = 0), and in the examples shown in FIGS. 8 and 9, x ₀ = 0. It is.

  The above-mentioned true cumulative demand curve (that is, the shortest broken line) can be obtained by using the following algorithm as an example.

-Algorithm for determining the true cumulative demand curve-
<Retention / update information>
○ Starting line Line ○ Upper limit line Upper limit demand and area boundary contact point ○ Lower limit line Lower limit demand and area boundary contact point
(1) Time update: Time = Time + 1
(2) Lower limit straight line <Upper limit accumulated demand value Demand from the starting point to the lower boundary contact point = Demand lower limit (confirmed)
Starting point = Boundary contact point of the lower limit straight line Time = Starting point, upper limit / lower limit straight line reset
Go to (1)
(3) Upper limit straight line> Lower limit cumulative demand Demand from the starting point to the upper boundary contact point = demand upper limit (confirmed)
Starting point = boundary contact point of upper limit straight line Time = resetting of starting point and upper / lower limit straight line
Go to (1)
(4) Lower limit straight line <Region lower limit Lower limit demand, lower limit boundary setting update Upper limit straight line> Region upper limit Upper limit demand, upper limit boundary setting update
(5) Return to (1) until the measurement time identifier t = T.

  In the present embodiment, the shortest broken line creation unit 31b uses the period corresponding to the analysis target section of the measurement data stored in the memory 35 in the process of S1 (that is, the combination data of the demand unit measurement value and the measurement time). Measurement data is read sequentially in time series.

  And the measurement time read from the memory 35 is converted into a serial number of 1, 2, 3,.

Subsequently, the measurement data subjected to the above conversion is used by the shortest broken line creation unit 31b to solve one of the optimization problems expressed by Equations 7 to 9, and the measurement time identifier t. The estimated power demand rate y _t [Wh / 30 minutes] is calculated for each.

Then, the value of the estimated estimated power demand rate y _t [Wh / 30 minutes] is associated with the value of the identifier t of the measurement time for the analysis target section by the shortest broken line creation unit 31b (in other words, measurement time as a combination data of the identifier value t and the value of the estimated power demand rate y _t) of is stored in the memory 35.

  Next, the time-specific determination unit 31c of the control unit 31 uses the estimated power demand rate for each measurement time calculated by the process of S2 to determine a time-specific determination index for the resident absence from home (S3). ).

The process of S3 is executed for each determination target time included in the analysis target section in which the process of S2 is executed. Incidentally, the identifier of the measurement time corresponding to the determination target time is denoted by t _e.

  In the present invention, when determining the absence from home at the determination target time, the estimated power demand rate data within a certain period before the determination target time is used, and the statistical amount such as the average value or the maximum value is the feature amount. And the feature amount is used to determine whether the user is at home or away. A characteristic amount calculation target period and a certain period before the determination target time is referred to as a “feature amount period”.

  The feature amount period is not limited to a specific time length [minute, time], and is appropriately set to an appropriate time length, for example, considering a prior analysis result. Specifically, for example, the feature amount period can be set to an appropriate time length in a range of about 1 to 3 hours, for example.

Specifically, for example, when performing the determination in a certain determination target time (identifier t = t _e of measurement time), when the feature quantity period is one hour, the identifier t of the determination target time (measurement time = t _e) the estimated power demand rate y _te in [Wh / 30 min] (the state of the determination target time before 30 minutes is reflected) and the previous measured time of the determination target time ( estimation identifier t = t _e -1) of measurement time power demand rate _{y te-1 [Wh / 30} ] ( Note that state from 60 minutes before the determination target time up to 30 minutes before is reflected) Are used to calculate a statistic about these two values as a feature quantity.

  The statistical amount as the feature amount is not limited to a specific type, and is appropriately set by taking up an appropriate type, for example, taking into account a prior analysis result. Specifically, as the feature amount, for example, an average value (that is, an average value of the estimated power demand speed for each measurement time included in the feature amount period), a maximum value (that is, for each measurement time included in the feature amount period) The largest value of the estimated power demand rate of the current value), the minimum value (that is, the smallest value of the estimated power demand rate for each measurement time included in the feature amount period), the range (that is, included in the feature amount period) The difference between the largest value and the smallest value of the estimated power demand speed by measurement time), standard deviation (that is, the standard deviation value of the estimated power demand speed by measurement time included in the feature period) Etc.

  Note that the number of feature values used in the process of S3 is not limited to a specific number, and may be one or more.

  In addition to various statistics relating to power demand (specifically, estimated power demand speed), various information may be additionally used as feature quantities. Examples of information types in this case include household identifiers, determination target time (actual time), temperature, season, and the like.

  The feature amount (in other words, an explanatory variable) calculated or set as described above is used to classify home presence and absence to determine whether home is absent.

  As a technique for performing the two-class classification, for example, a machine learning technique such as a random forest or a support vector machine (abbreviation of SVM: Support Vector Machine) can be used.

  When the machine learning method is used, the combination data of the actual measurement data of power demand by time and the status data regarding the actual absence at home at the time is the correct data (“teacher data” and “training data”). Called) is required. This correct answer data is collected, for example, through a fact-finding survey, a monitor survey, a questionnaire survey, or the like, or is prepared using existing observation data. Then, the correct answer data is used for learning in the machine learning method to create a determination criterion for two-class classification. The procedure of the machine learning technique used for the two-class classification, the contents of the determination criteria, and the like are defined in advance in the determination program 37 for the absence of the resident at home, for example.

In the present embodiment, the time-based determination unit 31c, among the combination data identifier value t of the measurement time for the stored analyte segment in the memory 35 and the value of the estimated power demand rate y _t in the process of S2, All estimated power demand speed values associated with the identifier of the measurement time corresponding to the feature amount period for the determination target time (in other words, included in the feature amount period) are read.

  Then, the time-based determination unit 31c uses the read estimated power demand speed value to calculate the feature amount used for the two-class classification.

  The time-based determination unit 31c further uses a machine learning technique to perform two-class classification, and determines whether or not a person exists in the target house or the target dwelling unit for the determination target time.

Then, by the time-based determination unit 31c, the result of the two-class classification (that is, the determination result of whether “at home” or “absent”) is used as a determination target time ( When correspondence is in (in other words an identifier t = t _e) of the measurement time, a combination data of the value of the variable representing the time-based determination indicator home absent a value of the identifier of the measurement time) is stored in the memory 35.

  As the processing in the computer, when the result of the two-class classification is “at home”, an appropriate numerical value (specifically, appropriately selected as a numerical value corresponding to “at home” as a variable indicating the time-based determination index) For example, if 1 is given and is “absent”, an appropriate numerical value (specifically, for example, 0 (zero) is selected as a numerical value corresponding to “absent” in the variable representing the time-based determination index. )) Is given.

  In this embodiment, subsequently, the absence start time and the absence end time are specified by using the determination index according to the absence of home time determined by the above-described processing, and the absence period is determined (S4). Thru | or S11; FIG. 6, FIG. 10).

  Specifically, first, the absence indicator determining unit 31d of the control unit 31 uses the determination indicator for each absence of home at the determination target time determined by the processing of S3, and the determination indicator for each time is “absence” Is determined (S4).

In the present embodiment, by the absence interval judgment unit 31d, a time-based determination indicator home absence of the identifier t _e of the measurement time of the stored the determination target time is associated to the memory 35 is read in the processing at S3 .

  When the read time-specific determination index is “absent” (S4: Yes), the processing step proceeds to the processing of S5.

  Then, as the process of S5, the absent section determination unit 31d determines whether the absent section index set / operated in the process of S8 described later is “absent”.

If the absent section index is not “absent” (S5: No), if the absence start time remains in the initialized state before the process of S1, the measurement time of the determination target time is set. the actual time corresponding to the identifier t (= t _e) the previous measurement time identifiers t of (= t _e -1) is stored in the memory 35 as missed start time (S6). Note that if the absence start time is not in the initialized state before the processing of S1, no operation is performed on the absence start time.

  Next, the home absence determination unit 31e of the control unit 31 determines whether the resident is away from home by using the time-based determination index of absence from home determined by the process of S3 (S7).

  The determination index for time of absence at home determined by the process of S3 is a result determined independently for each determination target time, and although the resident is at home, there are few electrical devices to be used for a short time. Or when there is no change in the usage mode of the electric device (in other words, when the change in the power demand rate is small), there is a possibility that the determination result is absent.

  For this reason, in this embodiment, the final determination of the resident's absence at home is “absence” continuously for a predetermined time before the determination target time in the determination index by time of absence from home. Or in other words (in other words, whether or not the determination of “absence” in the process of S3 is continued for a predetermined time). Here, the predetermined time for determining absence from home is referred to as a “determination period”.

  The determination period is not limited to a specific time length [time] (in other words, the number of determination target times), and is appropriately set to an appropriate time length considering, for example, a prior analysis result. Is done. Specifically, for example, the determination period may be set to an appropriate time length in a range of about 1 to 12 hours, and may be set to any time length of about 4 to 8 hours. Preferably, it is more preferably set to about 6 hours.

In the present embodiment, the presence / absence determination unit 31e includes, among the combined data of the value of the measurement time identifier t stored in the memory 35 in the process of S3 and the value of the variable indicating the determination indicator for absence / at home, with the value of the determination target time of the measurement time identifier t (= t _e) a variable representing the time-based determination indicator home absent which are associated is read corresponds to the determination period for the determination target time In other words, the values of the variables representing the time-based determination indexes for all absence from home that are associated with the measurement time identifiers t (= t _e -1, t _e -2, etc.) (included in the determination period) are read. It is.

  Then, the home absence determination unit 31e determines whether or not all the time-based determination indexes for home absence during the determination period for the determination target time are “absent”.

  If there is at least one “at home” among the time-based determination indices of absence from home during the determination period (S7: No), the processing step returns to the processing of S1.

On the other hand, if all of the time-based determination indices for absence from home during the determination period are “absence” (S7: Yes), it is determined that the resident is absent during the determination period. by home absence judgment unit 31e, the actual time corresponding to the identifier t measurement time of the determination target time (= t _e) conjunction absence interval indicator is stored in the memory 35 after having been "absent" The absence end time is stored in the memory 35 (S8), and then the processing step is returned to the processing of S1. It should be noted that the absence section index is set to “absent” as processing in the computer as an appropriate numerical value appropriately selected as a numerical value corresponding to “absence” in the variable representing the absent section index, specifically, for example, 1 Is given.

Further, in the process of S5, when the absent section determining unit 31d determines whether the absent section index is “absent” and the absent section index is “absent” (S5: Yes). , the actual time corresponding to the identifier t measurement time of the determination target time (= t _e) is stored in the memory 35 as missed end time (S9), back thereon in the processing step to the step S1 It is. In the process of S9, the absence end time set in the process of S8 or the previous process of S9 is updated.

  Also, in the process of S4, the absence section determination unit 31d determines whether or not the time-based determination index for absence at home at the determination target time is “absent”, and the time-based determination index is “absence”. If not (S4: No), the processing step proceeds to the processing of S10.

  Then, as the processing of S10, the absence period determining unit 31f of the control unit 31 determines whether or not the absent section index is “absent”. If the absent section index is “absent” (S10) : Yes), it is determined that the absence period is from the absence start time specified in the process of S6 described above to the absence end time specified in the process of S8 or S9 described above (S11).

  And, as a result of the determination of absence from home for the target house and the target dwelling unit, the combination data of the absence start time and the absence end time is associated with an identifier for distinguishing each absence period as necessary. It is recorded in a data file or the like and stored (saved) in the storage unit 32 or displayed on the display unit 34. Thereafter, the processing step returns to the processing of S1. At this time, the absent start time, absent end time, and absent section index are all reset, and the processing in the computer is returned to the initialized state before the processing of S1.

  On the other hand, when the absent section index is not “absent” (S10: No), the processing step is returned to the process of S1. At this time, if the absence start time is not in the initialized state before the process of S1, the absence start time is reset, and the process in the computer is returned to the initialized state before the process of S1. It is.

According to the determination method, determination apparatus, and determination program for the resident absence from home configured as described above, the measurement time at a predetermined time interval and the value output at each measurement time, and the predetermined power amount Combined data with the power demand measured using l [Wh] as a unit of measurement, that is, a change in the value of a predetermined time interval and a predetermined power amount in increments of l is sporadic (in other words, intermittent) and discrete is data, or in other words in the sporadic aspects of change in the (intermittent), and and the value of each measurement time based on the data is discrete power demand rate y _t occur is in a time series of change in value Since it can be calculated, it becomes possible to improve the reproducibility of the actual situation of power demand.

  According to the determination method, determination device, and determination program of the resident absence from home configured as described above, there is a person in the dwelling or the dwelling unit based on the power demand speed in which the actual situation is well reproduced. As a result, it is possible to estimate whether the resident is away from home by improving the reliability of the occupant's absence from home. .

  In addition, since the period from the absence start time to the absence end time is a period in which no electricity is used for human activities in the target house or target residence, there is no person in the target house or target residence. Or understand that there may be an abnormal situation where there is a person in the target house or target unit but the person is unable to move due to, for example, illness or injury You can do it. Therefore, when the length of time from the absence start time to the absence end time exceeds a predetermined threshold, it is detected as a safety confirmation event, and whether there is a long-term absence for the target house or target unit It is also possible to confirm the above. In other words, the resident absence-at-home determination method of the present invention can be used as a monitoring system for elderly people, for example.

  Although the above-described embodiment is an example of a preferred embodiment for carrying out the present invention, the embodiment of the present invention is not limited to the above-described embodiment, and the present invention is not deviated from the gist of the present invention. Various modifications can be made.

  For example, in the above-described embodiment, the determination by time for the resident absence at home is determined in the process of S3, and then the processes of S4 to S11 are further performed to specify the absence start time and the absence end time. However, the processes of S4 to S11 are not essential in the present invention. That is, one of the essential points of the present invention is that the determination index for each time is determined, and the processing of S4 to S11 is one of the ways of using the determination index for each time, but the determination for each time The method of using the index is not limited to this.

  In view of the above, embodiments of the present invention include an aspect in which a time-specific determination index is determined (that is, until the process of S3 in the above-described embodiment), or the process of S3 in the above-described embodiment. Subsequently, it is determined whether or not all the time-based determination indices for absence from home during the determination period are “absent” (that is, a process corresponding to the process of S7 in the above-described embodiment is executed. The absence from home during the determination period before the determination target time is determined, and these processes are repeated for each determination target time as the determination target time progresses, so that the information about the absence from home during the determination period prior to the determination target time is obtained. Updated) and the like.

  In addition, the main points of the processes of S4 to S11 in the above-described embodiment are for determining the absence of home so that the absence period can be determined using the determination index for time of absence of stay determined by the process of S3. The absence period at home is determined for each determination period while the determination period as the predetermined time is moved in the time axis direction with the progress of the determination target time, and the start of one determination period determined to be absent is the absence start time. The end is specified as the absence end time, or the start of the first determination period among a plurality of determination periods determined to be absent continuously is set as the absence start time and the end of the last determination period is ended as an absence end. It is to specify as time. Therefore, if it is possible to specify the absence start time and the absence end time while determining the absence at home for each determination period moving in the time axis direction, a specific example corresponding to the processing of S4 to S11 in the above-described embodiment. Such methods and procedures are not limited to those described as the above-described embodiment (including FIG. 10).

  In addition, the advantage of specifying the absence start time and the absence end time by using a determination period that has a predetermined time length and moves in the time axis direction is independent for each determination target time. Used for a short time even though the resident is at home, as in the case of determining absence from home (ie, without considering the situation at the time before the determination target time). It is to avoid a false determination that the absence is present in a situation where there are few electrical devices or there is no change in usage of the electrical devices (in other words, in a situation where the change in the power demand rate is small). This idea is that, as in the above-described embodiment, the change in value is sporadic (in other words, intermittent) and discrete, or in other words, the occurrence of the change in value is sporadic in the time series (intermittent). And can be applied only when data of various characteristics is used, not when continuous data created based on data whose value change mode is discrete is used. In other words, the method of determining absence at home for each determination target time is not limited to the method in the above-described embodiment.

For example, data that is inherently continuous, in other words, data in which the occurrence of a change in value is continuous in time series and the aspect of the change in value is gradual, specifically, for example, 1 minute The present invention can also be applied to the case where 1 Wh unit data output every time is used. In this case, without solving an optimization problem as in the above embodiment, only include the 30-minute interval time identifier t each power demand speed y _t [Wh / 30 min] as an example and one minute intervals The power demand rate y _t [Wh / min] for each identifier t at the time, or, in general, the power demand rate y _t [Wh / n minutes] for each identifier t at a time interval of n minutes. And the process after S3 in the above-mentioned embodiment may be performed similarly.

Or, the occurrence of a change in value is sporadic (intermittent) in time series, and the aspect of the change in value is gradual. Specifically, for example, it is output every 30 minutes as an example. This can also be applied to the case where 1 Wh unit data is used. In this case, without solving an optimization problem as in the above embodiment, is merely Taking as an example, the power demand rate for each identifier t time in 30 minute intervals y _t [Wh / 30 min] to obtain . And the process after S3 in the above-mentioned embodiment may be performed similarly.

10 Gas consumption usage decomposition device 17 Gas consumption usage decomposition program 30 Resident resident absence determination device 37 Resident resident absence determination program

Claims (13)

  The total gas acquired as a combination data of the measurement time at a predetermined time interval and the value output at each measurement time and the flow rate count number counted as 1 count for each predetermined flow rate by measurement in the main of the gas pipe From the measurement data of consumption, the combination data of the time zone in which the gas appliance connected to the indoor pipe branched from the main trunk is used is extracted as data of the continuous operation section, and the measurement time in the continuous operation section A value obtained by multiplying the cumulative value of the flow rate counts for each time and the predetermined flow rate as a lower limit, and a value obtained by adding the predetermined flow rate to the multiplied value as an upper limit; and The total length from the start point to the end point of the continuous operation section is the shortest and is estimated as the sum of the total gas consumption measured at each measurement time. A continuous line is estimated that coincides with the total gas consumption at each measurement time, and the flow velocity of the gas in the trunk is calculated as the slope of the straight line between the measurement times in the broken line. The heating continuous operation section is specified based on the process for determining whether the data of the section is winter or not and whether the gas flow rate is in a predetermined range for a predetermined time. A process in which the minimum value of the flow velocity of the gas in all the continuous heating operation sections specified in the data of the continuous operation section is set as the heating base flow speed, and the heating base flow speed and the hot water supply A process in which the size of the sum of the minimum flow rate and the gas flow rate is compared, a process in which the sizes of the hot water supply minimum flow rate and the gas flow rate are compared, a kitchen minimum flow rate and an intermittent heating maximum flow rate, Out of The use of the total gas consumption is classified into hot water, heating, and kitchen by performing a process in which at least one of the gas flow rates is compared. Gas consumption consumption decomposition method.   After the flow rate of the gas is calculated, a process of determining whether the data of the continuous operation section is the winter season is performed first, and a process of specifying the heating continuous operation section is performed next, The method according to claim 1, wherein the process of setting the heating base flow rate is performed subsequently.   The total gas acquired as a combination data of the measurement time at a predetermined time interval and the value output at each measurement time and the flow rate count number counted as 1 count for each predetermined flow rate by measurement in the main of the gas pipe Means for extracting, from the measurement data of consumption, the combination data of a time zone in which a gas appliance connected to an indoor pipe branched from the main trunk is used as data of a continuous operation section; The lower limit is a value obtained by multiplying the cumulative value of the flow rate counts at each measurement time by the predetermined flow rate, and the upper limit is a value obtained by adding the predetermined flow rate to the multiplied value. And the total length from the start point to the end point of the continuous operation section is the shortest, and the total of the total gas consumption measured at each measurement time, Means for calculating a gas flow velocity in the main trunk as a slope of a straight line between the measurement times in the broken line by estimating a broken line that coincides with a total gas consumption amount at each measurement time set It is determined whether or not the data of the operation section is winter, and the continuous heating operation section is specified based on whether or not the state where the gas flow velocity is in a predetermined range continues for a predetermined time. And the minimum value of the flow velocity of the gas in all the heating continuous operation sections specified in the data of the continuous operation section is set as the heating base flow speed, and the sum of the heating base flow speed and the hot water supply minimum flow speed and the The gas flow rate is compared, the hot water supply minimum flow rate is compared with the gas flow rate, and at least one of the kitchen minimum flow rate and the intermittent heating maximum flow rate is compared with the gas flow rate. And the by comparing the size, hot water supply amount of use of the total gas consumption, heating component, and gas consumption applications decomposing apparatus, comprising a means for classifying the kitchen minute.   After calculating the flow rate of the gas, a process of determining whether the data of the continuous operation section is the winter season is first performed, and a process of specifying the heating continuous operation section and setting the heating base flow speed is performed. The gas consumption consumption decomposition apparatus according to claim 3, which is performed next.   The total gas acquired as a combination data of the measurement time at a predetermined time interval and the value output at each measurement time and the flow rate count number counted as 1 count for each predetermined flow rate by measurement in the main of the gas pipe From the measurement data of consumption, a process of extracting the combination data of the time zone in which the gas appliance connected to the indoor pipe branched from the main is used as data of a continuous operation section, and the process in the continuous operation section The lower limit is a value obtained by multiplying the cumulative value of the flow rate counts at each measurement time by the predetermined flow rate, and the upper limit is a value obtained by adding the predetermined flow rate to the multiplied value. And the total length from the start point to the end point of the continuous operation section is the shortest, and the total of the total gas consumption measured at each measurement time, A process for calculating a gas flow velocity in the main trunk as a slope of a straight line between the measurement times in the broken line by estimating a broken line that matches a total gas consumption amount at each measurement time set It is determined whether or not the data of the operation section is winter, and the continuous heating operation section is specified based on whether or not the state where the gas flow velocity is in a predetermined range continues for a predetermined time. And the minimum value of the flow velocity of the gas in all the heating continuous operation sections specified in the data of the continuous operation section is set as the heating base flow speed, and the sum of the heating base flow speed and the hot water supply minimum flow speed and the The gas flow rate is compared, the hot water supply minimum flow rate is compared with the gas flow rate, and at least one of the kitchen minimum flow rate and the intermittent heating maximum flow rate is compared with the gas flow rate. By comparing the magnitude of the total gas consumption applications hot water fraction of, heating component, and gas consumption applications decomposer, characterized in that to perform a process of classifying the kitchen fraction to the computer.   After calculating the flow rate of the gas, a process of determining whether the data of the continuous operation section is the winter season is first performed, and a process of specifying the heating continuous operation section and setting the heating base flow speed is performed. 6. The use decomposition program for gas consumption according to claim 5, which is performed next.   From the measurement data of the power demand amount obtained as a combination data of the measurement time of the predetermined time interval and the value output at each measurement time and the power demand amount measured with the predetermined power amount as a measurement unit The combination data in the time zone is extracted as data of the analysis target section, and the cumulative value of the power demand amount at each measurement time in the analysis target section is set as a lower limit and the accumulation of the power demand amount at each measurement time The power demand that is present in a region whose upper limit is a value obtained by adding the predetermined amount of power to the value, has the shortest total length from the start point to the end point of the analysis target section, and is measured at each measurement time A line that matches the total amount of power demand estimated at each measurement time is estimated, and the power demand is calculated as the slope of the straight line between the measurement times on the line. The power demand rate included in the predetermined period before the measurement time that is the target of the absence of being at home is calculated, and the statistic of the power demand speed in the predetermined period is calculated as the feature amount. A method for determining whether a resident is away from home, wherein at least the feature amount is used to determine whether the resident is away from home.   The method for determining whether a resident is away from home according to claim 7, wherein the measurement data of the power demand is data output from a route A of a smart meter.   From the measurement data of the power demand amount obtained as a combination data of the measurement time of the predetermined time interval and the value output at each measurement time and the power demand amount measured with the predetermined power amount as a measurement unit The combination data in the time zone is extracted as data of the analysis target section, and the cumulative value of the power demand amount at each measurement time in the analysis target section is set as a lower limit and the accumulation of the power demand amount at each measurement time The power demand that is present in a region whose upper limit is a value obtained by adding the predetermined amount of power to the value, has the shortest total length from the start point to the end point of the analysis target section, and is measured at each measurement time The power demand speed is estimated as a straight line slope between the measurement times in the broken line by estimating a broken line in which the total amount of power and the estimated total power demand amount at the measured time coincide with each other. Calculating at least a statistic of the power demand rate in the predetermined period as a feature amount using the power demand speed included in a predetermined period before the measurement time that is a target of absence from home; And a means for determining whether the resident is away from home using the feature amount.   The determination device of the absence of a resident at home according to claim 9, wherein the measurement data of the power demand is data output from a route A of a smart meter.   From the measurement data of the power demand amount obtained as a combination data of the measurement time of the predetermined time interval and the value output at each measurement time and the power demand amount measured with the predetermined power amount as a measurement unit The combination data in the time zone is extracted as data of the analysis target section, and the cumulative value of the power demand amount at each measurement time in the analysis target section is set as a lower limit and the accumulation of the power demand amount at each measurement time The power demand that is present in a region whose upper limit is a value obtained by adding the predetermined amount of power to the value, has the shortest total length from the start point to the end point of the analysis target section, and is measured at each measurement time The power demand speed is estimated as a straight line slope between the measurement times in the broken line by estimating a broken line in which the total amount of power and the estimated total power demand amount at the measured time coincide with each other. And calculating a statistic of the power demand rate in the predetermined period as a feature amount using the power demand rate included in a predetermined period before the measurement time that is a target of determination of absence from home A program for determining the absence of a resident at home, which causes a computer to perform a process of determining whether the resident is away from home using the feature amount.   12. The non-resident presence-at-home determination program according to claim 11, wherein the power demand measurement data is data output from a route A of a smart meter.   Based on the measurement data of the power demand, the power demand rate for each time is calculated, and the power demand rate statistics in the predetermined period are used by using the power demand rate included in the predetermined period before the determination target time. An amount is calculated as a feature amount, and at least the feature amount is used to determine whether a resident is away from home for each determination target time, and corresponds to a determination period that moves in the time axis direction with a predetermined time length When all the determination results for each determination target time are absent, it is determined that they are absent during the determination period, and the start time of one of the determination periods determined as absent is set as the absence start time and ends. Is specified as the absence end time, or the start time of the first determination period among the plurality of determination periods determined to be absent continuously is set as the absence start time and the end Method of determining the period of absence, which comprises identifying a time of the end of the determination period as missed end time.