JP2020098631A - Energy consumption data processing system - Google Patents

Abstract

To provide an energy consumption data processing system that can discriminate an age composition and wake-up time of a household on the basis of energy consumption data at each time in one day of a past prescribed period of the household.SOLUTION: An energy consumption data processing method in an energy consumption data processing system is configured to: take in power consumption data in each time zone in one day by at least two weeks of at least summer and winter of a household; obtain deflection width serving as a differential absolute value between a maximum value of a power consumption amount at each time and a minimum value thereof, for the taken power consumption data; determine whether the deflection width in the time zone thought to be a sleeping time converges to be small in comparison with other time zone; when the deflection width converges to be small, determine that the household is an elderly household; and determine that the time zone in which the deflection width notably changes to great from small is a wake-up time of the household.SELECTED DRAWING: Figure 2

Description

The present invention relates to an energy consumption data processing system, and more specifically, it determines the age structure of a household and the time and situation of waking up based on the energy consumption data at each time of day of the past predetermined period of the household. The present invention relates to an energy consumption data processing system capable of accurately detecting changes in household life.

Recently, as the price of gas and electricity has increased due to the soaring price of crude oil, the awareness of energy saving is increasing in homes and businesses. Therefore, it is a business to provide a wide variety of services related to energy saving and power saving according to the demands of homes and companies. The content of the service is, for example, a method of using equipment/equipment (including a building) for reducing unnecessary energy consumption by analyzing data of power consumption in each time period of a customer's seasonal fixed period. Services, including maintenance/inspection of equipment/equipment, repair/replacement or new introduction of equipment/equipment, and mediation of equipment/equipment.
By the way, energy consumption data such as power consumption has a mirror effect that reflects the actual conditions of life of consumers, and consumes various changes in daily life such as at home, when absent, when visiting customers, at bedtime, and when waking up. The amount of electricity is reflected. For example, in household life, if there is a life change that is significantly different from the normal life (standard life) of the month, such as the year-end and New Year holidays, it will be affected, and energy consumption will change, and some signs will appear in the data. .. Therefore, in addition to the various consulting services for reducing the energy consumption described above, a so-called monitoring service that detects any abnormal or emergency situation of the customer from the data of the energy consumption of the customer is performed as a business. An invention relating to a dangerous state determination system related to a monitoring service is known (for example, see Patent Document 1).
In the above system, a histogram relating to the amount of power consumption for each day is generated from power time series data for the past N days (N: a positive integer) that has been arbitrarily set, and for each of the generated histograms for the amount of power consumption for each day. , Separation Metrics σ _b ² /σ _w using the Discriminant Analysis Method to separate into two classes, a low power consumption class with low power consumption and a high power consumption class with high power consumption ² (σ _b ² : Between-Class Variance), σ _w ² : Within-Class Variance) are calculated every day, and the calculated separation σ _b ² /σ _w ² By deriving the threshold for the day on which the maximum value is obtained, the derived threshold is considered to be the maximum value of the power consumption during automatic device operation of the electric devices installed in the target home. The first threshold t1 for determining the presence/absence of one of the dangerous states of a person who lives at home is set (Patent Document 1).

On the other hand, regarding the generated histogram of power consumption for each day, the minimum power consumption for each day is extracted as the minimum power consumption for each day, and the minimum power consumption is extracted from the extracted minimum power consumption for each day. Amount is extracted as the minimum power consumption amount for N days, and based on the extracted minimum power consumption amount, the minimum value of the power consumption amount during the automatic operation of the electric device installed in the target home is calculated. By calculating, the minimum value of the calculated power consumption during automatic device operation is set as the second threshold value t2 for determining the presence/absence of the other dangerous state of the person at home. Patent Document 1).

Then, when the amount of power consumption in the home exceeds a predetermined limit time, if the state of not exceeding the first threshold value continues, or if the state of not decreasing below the second threshold value continues, It is decided to determine that the person in the house is in a dangerous state where the person cannot operate the device (Patent Document 1).

JP, 2013-131096, A

According to the dangerous state determination system described in Patent Document 1, the state in which the power consumption in the house does not exceed the first threshold value t1 continues and exceeds a predetermined limit time, for example, one day (24 hours), It is said that the situation where the person inside the house does not perform any operation to start the operation of the device at all does not occur in the normal state unless a rare case occurs when the person inside the house stays out. The effect is described (cited document 1).

Further, the operation in which the power consumption in the house does not decrease below the second threshold value t2 and the person in the house stops the operation of the device after exceeding a predetermined limit time, for example, one day (24 hours). It is stated that the situation that does not happen at all is a situation that does not occur under normal conditions, except for special events where special events such as home parties are held all night. (Cited document 1).

However, in the dangerous state determination system, it is necessary to observe the transition of the power consumption in the house for a long time (for example, one day) in order to make a final determination. It seems that it is not possible to take prompt measures. In addition to the power consumption data for normal living (normal value), the power consumption data used for creating the histogram includes power consumption data related to hosting a home party (abnormal value of life) and electricity meter. It is considered that the power consumption data (measurement abnormal value) related to the erroneous measurement of or the missing power consumption data (missing value) is included, and these normal value, life abnormal value, measurement abnormal value and missing value are Histograms regarding power consumption for each day created based on power consumption data included in all, and further, the first threshold value t1 and the second threshold value t2 used when determining a dangerous state are data other than normal values. Since so-called noise is included, it is considered that there is room for further improvement in the determination result obtained from the dangerous state determination system in terms of accuracy, accuracy, and reliability.
Therefore, the present invention has been made in view of the above-mentioned problems of the conventional technology, and an object thereof is to determine the age structure of a household based on the energy consumption data at each time of day of the past predetermined period of the household. In addition to discriminating the time to wake up, the standard life model that reflects the normal life of the household accurately is used as a reference, while the energy consumption data of the household is captured in real time, based on the degree of deviation from the standard life model, the life of the household The object of the present invention is to provide an energy consumption data processing system capable of accurately detecting changes in the energy consumption.

In the energy consumption data processing system according to the present invention for achieving the above object, a data acquisition unit for acquiring energy consumption data of a household and an age structure, a wake-up time and a life of the household based on the energy consumption data. An energy consumption data processing system, comprising: a data operation unit for analyzing changes; and a data output unit for outputting an analysis result concerning changes in household life analyzed by the data operation unit, wherein the data operation unit is , The energy consumption data of each time zone of each day in the past predetermined period of the household is taken in, and the maximum and minimum values of the power consumption at each time corresponding to the sleeping hours of the energy consumption data taken in The fluctuation range which is the absolute difference and the increase/decrease change of the fluctuation range over the entire time period are obtained, and the age structure and the wake-up time of the household are determined based on the fluctuation range of the power consumption and the fluctuation change of the fluctuation range. It is characterized by

The inventor of the present application, the energy consumption data of each time zone in a predetermined period (for example, every month in summer and winter) for each household (for example, an elderly household and a young household (non-elderly household)) ( For example, as a result of diligent analysis of the fluctuation range relating to the power consumption data), it was found that a peculiar tendency exists. That is, regarding the fluctuation range related to the power consumption data of the elderly household, in the summer and winter, for example, in the time zone considered to be the bedtime from 1:00 am to 6:00 am, the fluctuation range of the power consumption is different from the other time. It is smaller and more compact than the obi. On the other hand, in the youth household, the peculiar tendency that the fluctuation range that appears in the elderly household is small and small is not seen, and the fluctuation range is almost constant throughout the whole time period and shows a relatively large value. There is.
Furthermore, it was found that there is a unique tendency in the fluctuation range of the fluctuation range of each household throughout the entire time period. That is, regarding the fluctuation range of the elderly household, as described above, the fluctuation range in the time zone considered to be bedtime is smaller than that in other time zones, but the fluctuation range at a certain time (time zone) is the boundary. It changes remarkably from small to large, and after that time, the swing width shows an almost constant value. On the other hand, the fluctuation of the fluctuation range of the youth household will be described later in detail, but the fluctuation range remarkably changes from small to large at a certain time (time zone) as seen in the elderly household. There is no clear tendency like this.
In other words, by analyzing the energy consumption of each time zone of a household over the past predetermined period of time and checking whether the fluctuation range of energy consumption is smaller than that of other time zones. The peculiar tendency indicates that the household can be distinguished as an elderly household or a young household. At the same time, the peculiar tendency indicates that it is possible to determine the wake-up time of the household from the time (time zone) at which the fluctuation range significantly changes from small to large.
Therefore, in the energy consumption data processing system of the present invention, the energy consumption data of a certain household in the past predetermined period is acquired, and the fluctuation range (maximum value and minimum value) of the energy consumption in the time zone corresponding to the sleeping time zone is acquired. , And the increase and decrease changes in the fluctuation range over the entire time period, the age structure of the household is determined and the wake-up time of the household is determined.

A second feature of the energy consumption data processing system according to the present invention is that the period is at least a two-week period in summer and winter.

Although details will be described later, there are some differences in the energy consumption data for summer and winter between elderly households and young households. In the above configuration, in order to determine the age structure of the household in consideration of the difference, the age structure of the household is determined based on the energy consumption amount of at least two weeks in summer and winter.

The third feature of the energy consumption data processing system according to the present invention is that the bedtime is from 1:00 am to 6:00 am.

In the above configuration, in order to easily distinguish the small fluctuation range, it is possible to distinguish the small fluctuation range focusing on the time zone where the energy consumption fluctuation range is particularly small. To do.

A fourth characteristic of the energy consumption data processing system according to the present invention is that the data calculation unit removes life abnormality data from the taken-in energy consumption data so that each day of the household has a daily life. Create a "standard life model" corresponding to the energy consumption load curve of the household, calculate the degree of deviation from the standard life model while capturing the energy consumption data of the household in real time, and based on the degree of deviation, the alarm level for the household Is to set.

In the above configuration, in order to prevent large-scale changes in household life that may occur in the future, create a model load curve (standard living model) that relates to energy consumption in each time zone of a normal life, Based on the standard life model as a standard, the degree of deviation from the standard life model for energy consumption data of the household captured in real time is obtained, and changes in household life are detected based on the degree of deviation, and the degree of deviation is determined. Set an appropriate alarm level according to the setting.

A fifth characteristic of the energy consumption data processing system according to the present invention is that the energy consumption is the main amount of electric power of a household.

In the above configuration, the main power consumption is always consumed and can be measured at all times among the energy consumed by a person, so that there is an advantage that it is easy to detect the age structure of the household, the wake-up time, and changes in the household life.

According to the energy consumption data processing system of the present invention, it becomes possible to preferably determine the age structure and wake-up time of a household based on the energy consumption data at each time point in the past predetermined period of the household.
In addition, when creating the above standard life model that accurately reflects the normal consumption life of a household based on the energy consumption data from which abnormal life values have been removed, while capturing the energy consumption data of the household in real time, It is possible to accurately detect changes in household life based on the degree of deviation from the standard life model, and to set an appropriate alarm level according to the degree of deviation from the standard life model. As a result, it is possible to prevent major changes in the lives of households in the future through energy consumption data.

It is a block explanatory view showing the composition of the energy consumption data processing system concerning the present invention. It is a flow figure showing power consumption data processing concerning the present invention. It is a graph which shows the amount of electric power consumption of an elderly household at each time of day according to the season. It is a graph which shows the power consumption of each time of day of a young household by season. It is explanatory drawing which each shows the power consumption data concerning a standard life, and the power consumption data concerning an extraordinary life. It is a graph which respectively shows the standard life model regarding a power consumption amount in a user's house in July, a life abnormality model, and each power consumption amount on July 6th and July 14th.

Hereinafter, the present invention will be described in more detail with reference to the embodiments shown in the drawings.

FIG. 1 is a block diagram showing the configuration of an energy consumption data processing system 100 according to the present invention. Although details will be described later with reference to FIG. 2 to FIG. 6, this energy consumption data processing system 100 is used in a predetermined period (two or more weeks in summer and winter, preferably one year) of the user's house 1. Each time of day (here, a time having a time width, for example, 0:00 to 1:00 for 0:00, ..., 23:00 to 24:00 for 23:00 has an hour width of 1 hour. ) Energy consumption (power consumption in this embodiment) based on the past measurement data (fluctuation range), the age structure and the wake-up time of the user home 1 can be determined, and the household of the user home 1 can be determined. A power consumption load curve (hereinafter, referred to as “standard life model”) at each time in a normal life of the user is created in advance, and while the power consumption data at each time of the user's home 1 is captured in real time, The degree of deviation of the power consumption data of the home 1 from the standard life model is evaluated in stages, and some sign (signature) related to the life change of the user home 1 is accurately detected, and the alarm level according to the evaluation result. Is transmitted to the monitoring unit 30, and the monitoring unit 30 takes appropriate measures according to the received alarm level, thereby preventing a major change in life that may occur in the user's home 1 in the future. is there. In particular, the age range of the user home 1 is checked by checking the fluctuation range related to the power consumption data in the time zone from 1 am to 6 am, which is the bedtime, and the increase/decrease change in the fluctuation range throughout all the time zones. And the wake-up time can be determined. Hereinafter, each component of this system will be described.

As the configuration of the system, a data processing server (power consumption data processing that acquires power consumption data from a power meter such as a smart meter of the user's home 1 and executes power consumption data processing according to the present invention described later (power consumption data processing). (Device) 10, a communication network 20 that enables bidirectional communication between the data processing server 10 and the user's home 1, and the user's home 1 according to the analysis and evaluation result related to the power consumption data by the data processing server 10. On the other hand, the monitoring unit 30 is provided with a predetermined measure and the user's home 1 that is a monitoring target of the monitoring unit 30. Hereinafter, each configuration will be described in more detail.

The user home 1 shown in FIG. 1 is provided with a power transmission/distribution line 9 to which power (commercial power) is supplied from the outside. The power transmission/distribution line 9 is a line for supplying commercial power from an electric power company. The in-home wiring 7 is a power distribution facility separated from the power transmission/distribution line 9 and a power receiving facility such as the distribution board 2, and is a facility for a resident of a house to use a power source. The distribution board 2 is a device that incorporates a branch circuit, a breaker, and the like for in-home power distribution.

The watt-hour meter 5 is a power measuring device that measures (measures) the power usage of the entire user home 1, that is, the total power usage used by the user. As such an electricity meter 5, a smart meter, which is a device for an electric power company or the like to automatically read the integrated electricity meter of each building through a communication network 20 described later, may be used.

The electric power meter 5 is connected to the communication device 6 via the communication line 8 and transmits measured power consumption (usage) data (measurement data) to the communication device 6. In the present embodiment, the watt-hour meter 5 measures the power consumption of only 15 minutes in one hour, and converts the measured power consumption data into one hour (60 minutes) at one-hour intervals. You may make it transmit to the communication apparatus 6.

The communication device 6 receives the power consumption data transmitted from the power meter 5 and transmits the received data to the data processing server 10 via the communication network 20. In addition, in the example shown in FIG. 1, the watt-hour meter 5 and the communication device 6 are configured separately, but they may be configured integrally. When the watt-hour meter 5 is a smart meter, both are integrated.

The data processing server 10 is a computer or the like for performing power consumption amount data processing described later on the power consumption amount data transmitted from the communication device 6 of the user home 1. The data processing server 10 is installed and operated, for example, by a business operator who conducts research and consulting on energy saving, a business operator who provides various information such as information for energy saving support, and the like. As shown in FIG. 1, the data processing server 10 receives the power consumption data transmitted from the communication device 6 via the communication network 20. The data processing server 10 includes a communication unit 11, a data processing unit 12, and a data storage unit 13.

The communication unit 11 is connected to the communication network 20 and modulates (encodes) the transmission data into a data format corresponding to the network standard, or demodulates (decodes) the reception data into a data format corresponding to its own system. It is a data transmission/reception device that transmits and receives data. Specifically, the communication unit 11 receives the power consumption amount data transmitted from the communication device 6 via the communication network 20, and causes the monitoring unit 30 to set the alarm level according to the analysis result of the power consumption amount data. Send.

The data processing unit 12 is a calculation processing unit that executes a calculation related to power consumption amount data processing described later on the power consumption amount data of the user home 1. As shown in FIG. 1, the data processing unit 12 includes a data acquisition unit 12a that acquires the power consumption data measured by the watt-hour meter 5 and original data of the power consumption acquired by the data acquisition unit 12a. A data calculation unit 12b that executes a calculation related to power consumption data processing to be described later, and a data output unit that outputs an analysis result related to the power consumption data calculated by the data calculation unit 12b to the communication unit 11 and the data storage unit 13. 12c and.

The data storage unit 13 includes an operating system that controls and controls both hardware and software, and a program related to power consumption amount data processing according to the present invention, which will be described later, as well as a power consumption amount acquired by the data acquisition unit 12a. It stores (stores) the original data and the analysis result of the power consumption calculated by the data calculation unit 12b. For example, various types of data such as a hard disk, a memory, a DVD disk, and a Blu-ray disk that can read and write data The storage medium includes a storage medium developed in the future.

The data processing unit 12 shown in FIG. 1 shows a part of the functional blocks of the data processing server 10. Here, only the functions necessary for the data processing of the present invention are extracted and shown, and the illustration and description of other functions are omitted. In addition to the illustrated data processing server 10, a general personal computer or the like may be used to provide the same function. In this case, the computer program executed by the data processing unit 12 is a program for causing the computer to function as each unit illustrated. Further, this computer program may be incorporated in an existing application program. Further, this computer program may be recorded in a computer-readable recording medium such as a CD-ROM and installed in the computer, or may be downloaded through a communication network such as the Internet.

The communication network 20 is, for example, the Internet, but it may be any network as long as two-way communication is possible between the data processing server 10 and the communication device 6 of the user home 1, and may be, for example, a wireless communication network.

The monitoring unit 30 is, for example, a so-called security company that specializes in observing and monitoring as a business, but may be a relative of the user home 1 or a care support company.

FIG. 2 is a flowchart showing the power consumption amount data processing according to the present invention.
First, in step S1, the power consumption data is fetched from the power meter 5 of the user's home 1 (target household). The power consumption amount data is power consumption amount [Wh] at least at past two weeks in the summer and winter, and preferably at one time (each time zone) of one day for one year.

Next, in step S2, the fluctuation range of the power consumption data at each time is obtained. The fluctuation range is obtained by calculating the difference between the maximum value and the minimum value of the power consumption at each time.
By the way, FIG. 3 is a graph showing power consumption data of the elderly household group at each time of day in summer and winter. FIG. 3A shows power consumption data in summer, and FIG. 3B shows power consumption data in winter. Each plot in each graph shows the maximum value, the average value, and the minimum value of the power consumption for each time. A vertical line connecting the maximum value and the minimum value shows the fluctuation range of the power consumption amount at that time. As is clear from these graphs, the fluctuation range at each time from 1:00 am to 6:00 am, which is considered to be a bedtime, is smaller than other time zones regardless of summer and winter. .. Also, looking at the increase and decrease changes in the fluctuation range over time, the fluctuation range markedly changed from small to large at a certain time (for example, the time of 7 am) regardless of summer and winter, and after that It can be seen that, for example, the swing width shows a substantially constant value in the time zone from 8:00 to 23:00. In other words, the characteristic that the fluctuation range remarkably changes from small to large indicates the wake-up time of the elderly household.

On the other hand, FIG. 4 is a graph showing power consumption data of a young household at each time of day in summer and winter. FIG. 4A shows power consumption data in summer, and FIG. 4B shows power consumption data in winter. As is clear from these graphs, the fluctuation range at each time in the time zone from 1:00 am to 6:00 am is smaller and smaller than each fluctuation range of the elderly household in the same time period in FIG. I understand. Also, looking at the change in the fluctuation range over time, the fluctuation range is almost zero except for the time zone from 9:00 to 17:00 in winter (Fig. 4(b)), and the fluctuation range does not depend on the time. , Shows a large and almost constant value. In other words, the fluctuation range of power consumption in young households is such that there is no small unit like that seen in the time zone from 1 am to 6 pm, which is considered to be the bedtime of elderly households. Even in the band, the fluctuation range is large and shows a substantially constant value. Therefore, in the power consumption data of a certain household, when the fluctuation range in the time zone considered as the bedtime is smaller than that in the other time zones, the household is considered to be an elderly household. In addition, the fluctuation range at each time in the time zone from 9:00 to 17:00 in the winter of the youth household (Fig. 4(b)) is small compared to other time zones, but the degree of the small unity is Not noticeable compared to elderly households. This small group of young households is considered to be due to the low home-resident rate of young households during this period.

Looking at the power consumption (absolute value) of the elderly household and the young household in each time zone, the electricity consumption at each time in the time zone from 8:00 to 17:00 shows that the elderly household is younger. While it is higher than that of households, the power consumption at each time in the time zone from 18:00 to 23:00 is higher in young households than in elderly households. This is thought to be because the home stay rate of elderly households is higher than that of younger households during the hours from 8:00 to 17:00, while the air conditioners for younger households during the hours of 18:00 to 23:00 ( This is probably because the usage rate of coolers or heaters is higher than that of elderly households.

As described above, the fluctuation range of the power consumption amount read from the power consumption data at each time point for each household shown in FIGS. 3 and 4, the fluctuation range of the fluctuation range, and the respective features of the power consumption amount are summarized below. It becomes the street.
(1) The fluctuation range in the time zone from 1 am to 6 pm, which is considered to be the bedtime zone of the elderly household, is smaller than other time zones regardless of summer and winter.
(1') The swing range of the elderly household in the time zone from 22:00 to 23:00 in winter is the maximum throughout the whole winter time zone.
(2) The fluctuation range of younger households shows a large and almost constant value compared to elderly households throughout the whole period except for the time zone from 8:00 to 17:00 in winter.
(2′) The absolute value of the power consumption of the elderly household at each time changes remarkably from small to large at a certain time, for example, at 7:00 am.
(3) Regarding fluctuations in the fluctuation range of the elderly household, the fluctuation range remarkably changes from small to large at a certain time, for example, 7 am, regardless of summer or winter.
(4) Regarding the increase or decrease in the fluctuation range of the youth household in winter, the fluctuation range changes from large to small at a certain time, for example, 8:00 am, and also changes from small to large at a certain time, for example, 18:00. Changes to.
(5) The power consumption of elderly households from 8:00 to 17:00 is greater than that of young households.
(6) The power consumption of young households during the summer hours from 18:00 to 23:00 is greater than that of elderly households.

Therefore, in the present embodiment, whether or not the user home 1 is an elderly household is analyzed by analyzing the power consumption data of the user home 1 captured in real time based on, for example, the characteristics of (1) and (3) above. , And the wake-up time of the household can be determined.

Next, in step S3, it is determined whether the fluctuation range of the power consumption amount, which is considered to be a sleeping time zone, is smaller than those in other time zones, and the magnitude of the absolute value is also determined. If the fluctuation range obtained in step S2 is less than or equal to a predetermined value (for example, the magnitude and variance of the statistically processed power consumption at each time), it is determined that the fluctuations are smaller than those in other time zones (YES). Then, the process proceeds to step S4, and it is determined that the user home 1 is an elderly household in step S4. On the other hand, if the fluctuation range is not less than the predetermined value, it is determined that the fluctuation range is not smaller than other time zones (No), and the process proceeds to step S11. The user home 1 is determined to be a non-elderly household in step S11, and the processing executed in steps S7 to S9 (processing for detecting a change in life while taking in current power consumption data) is particularly necessary. It is determined that there is none and the process ends (END).

Next, in step S5, the power consumption data relating to the standard life is extracted, and the power consumption model for each predetermined time zone of the standard life is created. As shown in FIG. 5A, the power consumption curve on the day when an event (for example, a home party during the year-end and New Year holidays) that is rarely performed in normal life is performed is the standard consumption at each time. It appears as a daily power consumption curve A that deviates from the power consumption curve group B. On the other hand, the power consumption curve in normal life where events such as home parties are not performed is the power consumption curve group B in which the power consumption at each time on each day falls within the standard fluctuation range. appear. Here, the power consumption curve group B is extracted as the power consumption data related to the standard life of the user's house 1. Then, with respect to the extracted power consumption curve group B, as shown in FIG. 5B, statistically processed representative values for each time are obtained, and the obtained representative values are plotted for each time as a curve. This is a power consumption model load curve (standard life model) for each predetermined time of a standard life of the user's home 1, which serves as a reference for the degree of deviation used in the processing from S7 to S9. The power consumption curve A is used as a life abnormality model described later.

Next, in step S6, the power consumption data relating to the extraordinary life is extracted, and the power consumption model load curve (life abnormality model) for each predetermined time zone of the extraordinary life of the user's home 1 is calculated. ) Is created. Here, the power consumption curve A shown in FIG. 5A serves as a power consumption model for each predetermined time period of one day in an extraordinary life.

Next, in step S7, the power meter 5 of the user's house 1 is accessed to fetch the current power consumption data.

Next, in step S8, it is determined whether or not the power consumption amount captured in step S7 is within the standard life range. The determination is made by comparing with the standard life model created in step S5. That is, when the captured power consumption at the analysis target time is within the range of the standard life model (Yes), it is determined that the analysis of the deviation degree is unnecessary, and the capture of the power consumption is continued. On the other hand, when the taken power consumption exceeds (exceeds) the standard life model (No), it is determined that analysis of the deviation degree is necessary, and the process proceeds to step S9.

Next, in step S9, the degree of deviation from the standard life model is analyzed. The analysis of the degree of deviation is performed based on the absolute value of the excess amount from the standard life model. The absolute value of the excess amount is calculated, for example, by calculating |(power consumption at the captured analysis target time)−(power consumption at the corresponding time in the standard life model)}|. Details will be described later with reference to FIG.

FIG. 6 is a graph showing the standard life model, the life abnormality model, and the power consumption amounts on July 6 and July 14 of the user's home 1 in July.
First, it can be seen that the power consumption amount (X) on July 6 is almost within the range of the standard life model (◇) and the life abnormality model (□). Note that the power consumption in the 9 o'clock time zone exceeds both the standard life model (◇) and the life abnormality model (□), but the degree of deviation from the standard life model (◇) is negligible. , That is, the normal range. Quantitatively, the excess amount V of the power consumption with respect to the standard life model (◇) is less than the reference set value A of the maximum excess amount V _max between the standard life model (◇) and the life abnormality model (□). The divergence level that is within the range of and the range of the life abnormality model (□) is defined as the “normal range” here.

On the other hand, regarding the power consumption (△) on July 14, the power consumption in the time zone from 0:00 to 10:00 is almost within the normal range of the standard life model (◇) or the life abnormality model (□). On the other hand, regarding the power consumption in the time zone of 11:00, the excess amount V with respect to the standard life model (◇) exceeds the reference set value A of the above-mentioned maximum excess amount V _max , and the life abnormality model (□) Has reached the boundary of. However, the deviation of power consumption in this time zone from the standard life model (◇) is not a serious level. Quantitatively, for example, the excess amount V of the power consumption with respect to the standard life model (◇) is equal to or larger than the reference set value A of the maximum excess amount V _max between the standard life model (◇) and the life abnormality model (□). The deviation level that is within the range of the reference setting value B and within the range of the life abnormality model (□) is defined as “deviation level 1” here. (A<<B; A and B are referenced from the database held by the observation target.)

On the other hand, regarding the power consumption (△) in the time zone from 13:00 to 19:00 on July 14, the degree of deviation from the standard life model (◇) exceeds the reference setting value B of the above-mentioned maximum excess amount V _max. The boundary of the abnormal model (□) is reached. Therefore, the degree of divergence of the power consumption in this time zone from the standard life model (◇) is set to the caution level. Quantitatively, for example, the excess amount V of the power consumption with respect to the standard life model (◇) is equal to or larger than the reference set value B of the maximum excess amount V _max between the standard life model (◇) and the life abnormality model (□). The deviation level within the range of the maximum excess amount V _max and within the range of the life abnormality model (□) is defined as “deviation level 2” here.

Regarding the power consumption (△) at 21:00 on July 14, the deviation degree from the standard life model (◇) exceeds the maximum excess amount V _max , and the life abnormality model (□) Beyond the boundaries of. Therefore, the divergence of power consumption in this time zone from the standard life model (◇) is set to a serious level. Quantitatively, for example, the excess amount V of the power consumption with respect to the standard life model (◇) exceeds the maximum excess amount V _max between the standard life model (◇) and the life abnormality model (□). Moreover, the deviation level exceeding the life abnormality model (□) is defined as “deviation level 3” here.

Returning to FIG. 2 again, finally, in step S10, the deviation level is replaced with an appropriate alarm level, and the analysis result is transmitted to the monitoring unit 30. For example, the deviation level 1 is set to "alarm level 1", and it is considered appropriate to carry out follow-up observation as a measure. Further, the above-mentioned divergence level 2 is set to "alarm level 2", and it is considered appropriate to make a telephone contact, for example, as a measure. Further, the deviation level 3 is set to "alarm level 3", and as a measure, for example, a home visit may be implemented.

As described above, according to the energy consumption data processing system 100 of the present invention, it is possible to preferably determine the age structure and wake-up time of a household based on the fluctuation range of the energy consumption data at each time of the household. become.
In addition, when creating the above standard life model that accurately reflects the normal consumption life of a household based on the energy consumption data from which abnormal life values have been removed, while capturing the energy consumption data of the household in real time, It is possible to accurately detect changes in household life based on the degree of deviation from the standard life model, and to set an appropriate alarm level according to the degree of deviation from the standard life model. As a result, it is possible to prevent major changes in the lives of households in the future by using energy consumption data.

The embodiment of the present invention is not limited to the above, and various modifications are included within the scope not departing from the gist of the features of the present invention. For example, in step S3, the age structure and wake-up time of the household are determined based on the fluctuation range of the power consumption, but the age structure and wake-up time of the household may be determined based on the absolute value of the power consumption. Alternatively, it is possible to determine the age structure and wake-up time of the household based on the fluctuation range and the absolute value of the power consumption. Further, although electric power is selected as the energy consumption data in the above-described embodiment, it is not limited to electric power, and it is possible to select water, gas, or kerosene. Further, the degree of deviation (deviation level) from the standard life model is not limited to the above embodiment, but can be individually and specifically determined according to the attributes of the household and the specific living behavior. ..

1 User House 2 Distribution Board 5 Electricity Meter 6 Communication Device 7 Home Wiring 8 Communication Line 9 Transmission and Distribution Line 10 Data Processing Server (Energy Consumption Data Processing Device)
11 communication unit 12 data processing unit 12a data acquisition unit 12b data calculation unit 12c data output unit 13 data storage unit 20 communication network 30 monitoring unit 100 energy consumption data processing system

Claims (5)

A data acquisition unit that acquires energy consumption data of a household, a data calculation unit that analyzes the age composition, wake-up time, and life change of the household based on the energy consumption data, and the life of the household analyzed by the data calculation unit An energy consumption data processing system comprising: a data output unit that outputs an analysis result related to a change;
The data calculation unit captures energy consumption data of each time zone for each day in a predetermined period in the past of the household, and regarding the captured energy consumption data, the maximum value of the power consumption at each time corresponding to the bedtime zone. The difference between the minimum value and the fluctuation range is the absolute value of the fluctuation range and the increase or decrease change of the fluctuation range over the entire time period is obtained, based on the fluctuation range of the power consumption and the fluctuation range of the fluctuation range of the household age and An energy consumption data processing system characterized by determining the wake-up time. The energy consumption data processing system according to claim 1, wherein the period is at least a two-week period in summer and winter. The energy consumption data processing system according to claim 1, wherein the bedtime period is a time period from 1 am to 6 am. The data calculation unit removes life abnormality data from the imported energy consumption data and creates a “standard life model” corresponding to the energy consumption load curve for each hour of daily life in a household. The method according to any one of claims 1 to 3, wherein the degree of deviation from the standard life model is obtained while capturing the energy consumption data of the household in real time, and the alarm level for the household is set based on the degree of deviation. Energy consumption data processing system described. The energy consumption data processing system according to any one of claims 1 to 4, wherein the energy consumption is a main electricity amount of a household.

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