JP2007219912A - Energy supply evaluation system - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide an energy supply evaluation system capable of performing comparative evaluation of a plurality of energy supply forms while considering difference of life styles due to regional difference. <P>SOLUTION: The energy supply evaluation system is provided with a power consumption estimation means for estimating power consumptions classified by time zones in a day on the basis of prescribed first data and third data and a heat consumption estimation means 3 for estimating heat consumptions classified by time zones in a day on the basis of prescribed second data and third data, in the case that both energy media and power are supplied to a power and heat consumer from the outside. A first evaluation value calculation means 4 and a second evaluation value calculation means 5 calculate required amounts of power and energy media to demand of power and heat estimated in supplying either one of power or heat from energy media and supply of both heat and power from energy media , to calculate a prescribed evaluation value. <P>COPYRIGHT: (C)2007,JPO&INPIT

Description

  The present invention relates to an energy supply evaluation system that estimates energy demands such as electric power and heat, and evaluates usage costs such as usage charges for a plurality of energy supply modes to energy consumers.

Conventionally, energy consumed in ordinary households is supplied in the form of electric power, city gas, etc. from an electric power company or a gas company and is individually consumed. By the way, recently, a distributed energy system aimed at reducing CO ₂ emissions and saving energy has been actively developed and put into practical use, and power is generated in a power consuming area even in ordinary homes, apartment houses, offices, etc. The use of distributed power generation systems is expected to progress rapidly in the future. In particular, gas engine cogeneration systems and the like that can supply both heat and electricity are attracting attention because of their high overall energy efficiency because they can effectively use not only electric power but also thermal energy generated by the gas engine.

As described above, the diversification of energy supply forms in general households greatly improves energy costs and environmental costs such as CO ₂ emissions due to the consistency of energy demand trends in households and energy supply forms. There is room for it. Therefore, by accurately grasping the energy demand trend in the home, it is possible to select an energy supply mode that is optimal for environmental performance such as energy cost and CO ₂ emission.

  As an estimation method of energy demand in the home, for example, as in the estimation method disclosed in Patent Document 1 below, from the energy consumer's action schedule and the energy consumption of the equipment accompanying the action, A method for estimating energy consumption (demand) has been proposed. For example, the behavior schedule table illustrated in FIG. 16 includes behavior schedule data in which a schedule for each member of the household is input, and further, the devices associated with the behavior and the energy consumption thereof as illustrated in FIG. Data is created, and energy consumption is estimated based on these data.

JP 2003-125535 A

  However, in the conventional estimation method described above, it is necessary to register a large number of energy consuming devices, and it is difficult to grasp the energy consumer's behavior schedule, so general behavior data (for example, The National Lifetime Survey Report, etc., surveyed by the NHK Broadcasting Culture Research Institute is often used, and the energy consumption, which is the output as an estimation result, is different from the consumption experienced by consumers in the past. There may be doubts about authenticity.

  Also in Japan, for example, Hokkaido and Hokuriku have different lifestyles, so the method of estimating based on general behavior data is not suitable for the simulation of utility costs in many areas.

  FIG. 18 is a graph showing regional characteristics of heating use. FIG. 18A is a graph (1998) in which annual energy invested in heating is compared for each region, and FIG. 18B is FIG. It is the graph which compared the value (henceforth "the temperature sensitivity of heating demand") which divided the annual heating demand shown by a) by heat degree day (henceforth "HDD") for every area.

  Incidentally, the data on the amount of annual energy invested in heating is based on the data of 1998 of “Annual Energy Statistics for Homes 2003” (author: Research Institute for Living Environment Planning). The HDD data is obtained by quoting data from “Science Chronology 1998” (author: National Astronomical Observatory).

  Here, the HDD is a value obtained by accumulating a deviation (14 ° C.−daily average temperature) when the reference temperature for heating is set to 14 ° C. and the temperature is lower than that. In other words, the value of annual heating demand divided by HDD represents the energy temperature sensitivity of heating, and if the lifestyle is the same in all regions, the temperature sensitivity of heating demand varies greatly from region to region. Although it should not exist, according to FIG.18 (b), it turns out that there is actually dispersion | variation by area.

  For example, although Hokkaido uses more heating than Tohoku and Hokuriku, the temperature sensitivity of heating demand is lower than both regions. The reason for this is considered to be the difference in heat insulation of the house. In this way, the temperature sensitivity of the heating demand varies from region to region. In other words, it can be said that there are differences in how to use heating depending on the region.

  FIG. 19 is a graph showing regional characteristics of cooling use. FIG. 19A is a graph (1998) comparing annual energy invested in cooling by region, and FIG. 19B shows the annual cooling demand shown in FIG. , Abbreviated as “CDD”) (hereinafter referred to as “temperature sensitivity of cooling demand”) divided by region. Note that the annual energy amount data invested in cooling is the same source as the annual energy amount data invested in heating, and the HDD data is the same source as the CDD data.

  CDD is a value obtained by accumulating deviations (daily average temperature −24 ° C.) when the reference temperature for cooling is set to 24 ° C. and the temperature is higher than that. That is, as with the temperature sensitivity of the heating demand, the value obtained by dividing the annual cooling demand by CDD represents the energy demand sensitivity for cooling. If the lifestyle is the same in all regions, the temperature sensitivity of the cooling demand is Although there should be no significant difference between regions, according to FIG. 19 (b), it can be seen that there are actually variations among regions.

  For example, according to Fig. 19 (a), Kinki and Shikoku have the same cooling demand, but according to Fig. 19 (b), when comparing the temperature sensitivity of cooling demand, Kinki is more than 20% compared to Shikoku. I understand that it is small.

  In this way, explaining the difference in heating demand or cooling demand by region from the viewpoint of individual behavior such as the National Behavior Survey creates an average image of life after examining individual behavior in all regions It is necessary and extremely difficult.

  In view of the above problems, an object of the present invention is to provide an energy supply evaluation system capable of comparing and evaluating a plurality of energy supply forms in consideration of lifestyle differences caused by regional differences. . In particular, an object of the present invention is to provide an energy supply evaluation system that can simplify data input required for evaluation and has high reliability of evaluation results.

  In order to achieve the above object, an energy supply evaluation system according to the present invention provides a power demand and a heat demand in the case where both an energy medium capable of generating power and heat and power are supplied from the outside. As an energy supply form for the first energy supply form, the first energy supply form in which the energy medium generates only one of electric power and heat, and a second energy supply form in which the energy medium generates both electric power and heat, respectively. An energy supply evaluation system for evaluating costs, wherein a power usage amount estimating means for estimating a power usage amount for each time period of a day by performing arithmetic processing based on predetermined first data, and a time of a day Heat use amount estimating means for estimating the heat use amount of each band based on predetermined second data, and the daily power estimated by the power use amount estimating means. Each required supply amount of the daily energy and the energy medium supplied from the outside in order to cover the usage amount and the daily heat usage amount estimated by the heat usage amount estimation means in the first energy supply mode. A first evaluation value calculating means for calculating a predetermined evaluation value for use cost evaluation, a daily power usage estimated by the power usage estimation means, and a heat usage estimation means In order to cover the estimated daily heat usage with the second energy supply form, the daily power supplied from the outside and each required supply amount of the energy medium are calculated, and the usage cost evaluation is predetermined. Second evaluation value calculation means for calculating the evaluation value of the power consumption amount estimation means, and at least one means among the power usage amount estimation means and the heat usage amount estimation means is input to the predetermined third data inputted Depending on power usage or Changing the processing method for the estimation of the amount to the first feature.

  According to the first characteristic configuration of the system of the present invention, the first evaluation value is based on the power usage amount and the heat usage amount for each time zone estimated by the power usage amount estimation unit and the heat usage amount estimation unit. An energy use cost evaluation value in the first energy supply form in which the calculation means generates heat energy that covers internal power demand by supplying power from the outside and covers internal heat demand by supplying energy from the outside, for example. And the second evaluation value calculating means covers the internal power demand by both the external power supply and the power generated by the external energy medium supply, and the internal energy demand by the external energy medium supply. Since the energy use cost evaluation value in the second energy supply form that generates thermal energy to cover the demand is calculated, the energy use cost evaluation value in both energy supply forms is easily compared. Bets can be, can provide energy supply evaluation system can select the economically or high energy supplying form environmentally compatible adapted to the energy demand trend of the energy consumer.

  In particular, since at least one of the power usage estimation means and the heat usage estimation means is configured to be able to change the calculation processing method according to the third data, the degree of influence on the power usage and the heat usage is increased. When there is a strong factor, it is possible to estimate a more realistic power usage amount or heat usage amount by using the factor as the third data.

  At this time, the first data includes information on the family structure of energy consumers, information on the presence or absence or number of people staying in the daytime, information on the floor area, and information on the actual power usage amount or the power usage fee for a predetermined period. It can be. On the other hand, the second data includes information on the family structure of the energy consumer, information on the presence or number of people staying in the daytime, information on the floor area, and the actual usage value of the energy medium or the use of the energy medium in a predetermined period. It may include fee information.

  Further, in the energy supply evaluation system according to the present invention, in addition to the first feature, the third data indicates a difference in information for identifying a lifestyle of the energy consumer or a difference in geographical information. The second feature is that it is information for identification.

  According to the second characteristic configuration of the system of the present invention, as the third data, information for identifying a difference in lifestyle of energy consumers, or a difference in lifestyle in order to identify a difference in geographical information, or It is possible to estimate the amount of power used or the amount of heat used in consideration of differences in geographical information.

  In addition to the first or second feature, the energy supply evaluation system according to the present invention is configured such that the power usage amount estimation unit changes a calculation processing method according to the third data, Among a plurality of time zones obtained by dividing one day, a plurality of first characteristic time zones having a large amount of power usage determined based on a daily life pattern of the energy consumer and a plurality of second times having a small amount of power usage. A power usage estimation unit for each time zone that estimates each power usage amount in a characteristic time zone based on the first data per arbitrary day, and a plurality of power usages by time zone estimated by the usage usage estimation unit for each time zone The usage pattern estimation means for estimating the fluctuation pattern of the power usage in all the time zones of the arbitrary day based on the quantity, and the usage pattern estimation means for estimating the first cumulative power usage for a predetermined period Before In the predetermined period calculated based on the period power usage calculating means for calculating based on the fluctuation pattern, the first power cumulative usage, and the past usage record or a value determined according to the past use record Power accumulated usage amount comparison means for comparing the second power accumulated usage amount to calculate the error, and a correction input for all or a part of the estimated power usage amount by time period, or based on the error And a power estimation correcting means for correcting an estimated amount of the fluctuation pattern of the power usage amount.

  According to the third characteristic configuration of the system of the present invention, the peak demand (electric power consumption by time period in the first characteristic time zone) of a typical daily power demand curve (pattern) in a general household and For bottom demand (time-specific power usage in the second characteristic time zone), simply by inputting the predetermined input data for estimating each peak demand and bottom demand, the hourly usage estimation means And the bottom demand is estimated, and the usage pattern estimation means interpolates the power usage by time zone during that time period, so it is not necessary to input the daily action schedule of each member of the energy consumer in detail, The fluctuation pattern of the daily power consumption can be estimated.

  Furthermore, the period power consumption calculating means calculates the accumulated power usage for a specific period from the estimated daily fluctuation pattern, and the power accumulated usage comparing means is determined according to the past use record or the past use record. The power estimation correction means is configured to be able to correct each peak demand and bottom demand estimated by the hourly usage estimation means. Rather than correcting the input data and re-inputting, the error is calculated again by correcting each estimated peak demand and bottom demand manually or automatically based on the calculated error, and finally the error Therefore, it is possible to easily estimate the power usage amount by time of the day, which has high reliability with no difference from the past use record or the value determined according to the past use record.

  In addition, in the estimation of the daily power usage amount, the variation in the power usage amount by time zone in the first and second characteristic time zones (particularly, the first characteristic time zone) of the day in the estimation error of the entire period. By correcting each peak demand and bottom demand in the characteristic time period, the estimation of the fluctuation pattern of the daily power consumption is corrected and the cumulative power usage over the entire period It is intended to prevent a deviation from a past usage record or a value determined according to a past use record.

  The energy supply evaluation system according to the present invention is characterized in that, in addition to the third feature configuration, the second accumulated power consumption is calculated from a power usage fee for the predetermined period. To do.

  According to the fourth characteristic configuration of the system of the present invention, even if the past energy usage value of the energy consumer is not known, for example, if the annual power usage fee is known, based on this value Comparing the calculated annual power consumption with the first cumulative power consumption, and making corrections using the power estimation correction means based on the error found by the comparison, the power usage for one day It is possible to bring the estimation result of the amount variation pattern closer to the actual demand amount of the target energy consumer.

  Further, in the energy supply evaluation system according to the present invention, in addition to the third or fourth feature configuration, the power estimation correction unit may calculate the error calculated by the accumulated power usage comparison unit as a power usage amount. The power usage by time period in the first or second characteristic time zone is apportioned according to the degree of variation, and the estimated amount of the fluctuation pattern of the power usage is corrected based on the apportioned error. This is a fifth feature.

  At this time, a value (for example, standard deviation) relating to variation in power usage is calculated from a plurality of samples acquired in advance according to the first data and the third data, and this value is registered. When the error correction is performed by the estimation correction means, the estimated value of the variation pattern can be corrected by reading a value related to the variation.

  According to the fifth characteristic configuration, the past results are obtained by apportioning the error calculated by the accumulated power consumption comparison unit according to a predetermined amount of variation in the power consumption. Realistic correction reflecting the above can be performed.

  Further, in the energy supply evaluation system according to the present invention, in addition to any one of the third to fifth features, the use amount estimating unit for each time zone may perform the first feature time according to the third data. The sixth feature is to change the zone and the second feature time zone.

  According to the sixth feature configuration of the system of the present invention, when the first feature time zone and the second feature time zone have a difference according to the third data, the time zone according to the third data is used. Therefore, more realistic estimation is possible. This is particularly effective when the third data is geographical information.

  Further, in the energy supply evaluation system according to the present invention, in addition to any one of the third to sixth features, the hourly use amount estimation means may include a part or all of the first data. In accordance with the third data, each power usage amount in the plurality of first feature time zones and the plurality of second feature time zones is derived separately based on a first regression equation having elements as variables. The seventh feature is that the first regression equation is changed.

  According to the seventh characteristic configuration of the system of the present invention, the power usage amount estimating means easily considers the value of the third data by changing the first regression equation according to the third data. Can be estimated.

  Further, in the energy supply evaluation system according to the present invention, in addition to any one of the third to sixth characteristic configurations, the hourly usage amount estimation means may include a part or all of the first data. In accordance with the third data, each power usage amount in the plurality of first feature time zones and the plurality of second feature time zones is derived separately based on a first regression equation having a variable as a variable. The seventh feature is that the first regression equation is changed.

  Further, in the energy supply evaluation system according to the present invention, in addition to any one of the third to seventh characteristics, the predetermined period is a specific period to which the arbitrary one day belongs, and the power usage amount estimation Based on the estimated power usage by time zone of the plurality of first characteristic time zones and the plurality of second characteristic time zones, the means uses the fluctuation pattern of the power usage amount in a period other than the specific period. Second usage pattern estimation means for estimating the first feature, wherein the second usage pattern estimation means is based on a difference in average temperature between a period other than the specific period and the specific period. A correction value for correcting the power usage amount by time zone in the time zone and the second characteristic time zone is calculated, and the other value is calculated based on the plurality of power usage amounts by time zone after correction based on the correction value. Power usage for all time periods during the day To estimate the amount of fluctuation pattern and eighth features.

  According to the eighth characteristic configuration of the system of the present invention, only by inputting the average temperature data of the area where the household subject to the energy supply evaluation exists, or by inputting in advance, the amount of power used once In this estimation process, it is possible to estimate the fluctuation pattern of the power consumption in each period of one year.

  Further, in the energy supply evaluation system according to the present invention, in addition to the eighth feature, the second usage pattern estimation means includes a second or a second part of the first data having a variable as a variable. A ninth feature is that the correction value is derived based on a regression equation, and the second regression equation is changed according to the third data.

  According to the ninth characteristic configuration of the system of the present invention, since the value of the third data can also be taken into account for the calculation of the temperature correction value, particularly when the third data is geographical information highly correlated with the temperature. Therefore, more realistic power consumption estimation processing becomes possible.

  Further, in the energy supply evaluation system according to the present invention, in addition to the third or fourth feature configuration, the power estimation correction means decomposes the second power cumulative usage amount according to use and the power cumulative usage amount. The error calculated by the amount comparison means is apportioned according to the degree of variation for each application, and the estimated amount of the fluctuation pattern of the power consumption is corrected based on the apportioned error. Features.

  According to the tenth characteristic configuration of the system of the present invention, since the power estimation correcting means distributes the error according to the size of the variation for each application, more realistic error correction is possible. In particular, an error corresponding to the degree of variation of the energy consumer under the same condition by calculating in advance the size of the degree of variation for each use according to the values of the first data and the third data of the energy consumer. Since correction can be performed, more realistic error correction can be performed.

  At this time, as the size of the variation for each application, a value calculated from past power consumption actual values of a plurality of energy consumers under the same conditions may be used.

  In addition to the tenth feature configuration, the energy supply evaluation system according to the present invention may be used throughout the year when the power estimation / correction means serves as an index when decomposing the second cumulative power consumption. The eleventh feature is that it is composed of a base load whose amount is substantially constant, a cooling load whose use amount is recognized in summer, and a heating load whose use amount is recognized in winter.

  According to the eleventh characteristic configuration of the system of the present invention, the power usage amount for each application can be easily calculated from the annual power usage amount, and thereby the value of the magnitude of the variation for each application. Can also be easily calculated. For example, a load with little fluctuation throughout the year is defined as a base load, and the base load is a value of the monthly power usage indicating the minimum power usage. On the other hand, the heating load corresponds to the difference between the amount of power used in the month when the average temperature is 14 ° C. or less and the magnitude of the base load, and the cooling load is the amount of power used in the month where the average temperature is 24 ° C. or more. And the difference between the base load and the magnitude of the base load.

  Moreover, the energy supply evaluation system according to the present invention is configured such that, in addition to any one of the first to eleventh features, the heat use amount estimation means changes an arithmetic processing method according to the third data. In addition, the total heat usage related to the daily kitchen is calculated by a third regression equation using a part of the second data as a variable, and the total heat used for the daily hot water supply The usage amount is calculated by the actual usage amount value of the energy medium in the intermediate period in which no air conditioner is used, the estimation target period, the average temperature or average water temperature data of the intermediate period, and the kitchen heat usage estimation means The hot water supply heat usage estimation means that calculates based on the total heat usage related to the daily kitchen, and the intermediate period and heating that the power usage estimation means estimates the total heat usage related to the daily heating. Electricity usage for each day of the winter season Heating heat usage estimation means for calculating based on the difference in quantity, and at least one of the kitchen heat usage estimation means, the hot water supply heat usage estimation means, and the heating heat usage estimation means According to a twelfth feature, the means changes a third regression equation or a calculation equation used for calculation according to the third data.

  According to the twelfth characteristic configuration of the system of the present invention, the heat demand in a general household is divided into a kitchen heat use amount, a hot water supply heat use amount, and a heating heat use amount (only in the winter season), so that It is possible to estimate the heat demand according to the actual life, and in particular, it is possible to estimate in consideration of the third data.

  Moreover, the energy supply evaluation system according to the system of the present invention is characterized in that, in addition to any one of the third to eleventh feature configurations, the heat use amount estimation means calculates a total heat use amount related to a daily kitchen, A kitchen heat usage estimation means for calculating by a third regression equation using a part of the second data as a variable, a total heat usage related to hot water supply for a day, and a part of the second data as a variable. Heating heat amount calculating means for calculating hot water supply heat amount calculated by four regression equations, and heating heat for calculating the total heat usage amount for daily heating based on the value of electric power consumption amount by time zone estimated by the power usage amount estimating means Use amount estimation means, wherein at least one of the kitchen heat use amount estimation means, the hot water supply heat use amount estimation means, and the heating heat use amount estimation means corresponds to the third data. Change the 3rd regression equation, 4th regression equation, or calculation formula used for calculation And thirteenth aspect of the Rukoto.

  According to the thirteenth characteristic configuration of the system of the present invention, the heat demand in a general household is divided into a kitchen heat use amount, a hot water supply heat use amount, and a heating heat use amount (only in winter). It is possible to estimate the heat demand according to the actual life, and in particular, it is possible to estimate in consideration of the third data.

  Further, in the energy supply evaluation system according to the system of the present invention, in addition to the thirteenth feature configuration, the kitchen heat usage estimation means and the hot water supply heat usage estimation means are in a time zone where the heat usage is large. The four characteristic time zone is determined based on the first characteristic time zone as a fourteenth feature.

  According to the fourteenth characteristic configuration of the system of the present invention, the third characteristic time zone, which is the peak time zone of the heat usage by time zone of the kitchen heat usage and the hot water usage, is determined by the power usage estimation means. By determining on the basis of the first characteristic time zone, it is possible to have a relationship between the peak time zone of power usage and the peak time zone of heat usage, so that the reality is further reflected in the estimation result be able to.

  In addition to the thirteenth or fourteenth feature configuration, the energy supply evaluation system according to the system of the present invention is configured such that the total amount of kitchen heat usage for a predetermined period is estimated by the kitchen heat usage estimation means. Based on the amount estimated by the hot water supply heat usage estimation means, and the total amount of heating heat usage related to the predetermined period is calculated as the heating heat. Periodic heat usage amount calculating means for calculating based on the amount estimated by the usage amount estimating means, and hot water supply related to the predetermined period calculated based on the past use record or a value determined according to the past use record Heat use amount, kitchen heat use amount, heating heat use amount, and the value of each use amount calculated by the period heat use amount calculating means, and a heat accumulated use amount comparing means for calculating the error , The error It is prorated according to the size of the variation for each use of hot water, kitchen, and heating, and based on the apportioned error, the total heat consumption for the kitchen for the day, the total heat for the hot water supply for the day A fifteenth feature is provided with heat estimation correcting means for correcting the usage amount and the total heat usage amount related to the heating of the day.

  According to the fifteenth characteristic configuration of the system of the present invention, the estimated heat use amount and the past past use record or past use record of the energy consumer (for example, gas use fee) If there is an error between the amount of heat used and the amount of heat used, the energy is calculated for each application and corrected by apportioning the error according to the degree of variation for each application. It is possible to calculate an estimated heat usage amount close to the actual value of the consumer. In particular, the error correction can be performed more strictly by configuring the error correction for each application.

  The energy supply evaluation program according to the present invention for achieving the above object realizes each means of the energy supply evaluation system having any one of the first to fifteenth features by software processing on a predetermined computer. The program step for making it contain is characterized by the above-mentioned.

  According to the energy supply evaluation program according to the present invention, the energy supply evaluation system having the first to fifteenth feature configurations can be realized on the computer by installing the program in a predetermined computer. The effect of the energy supply evaluation system of the 1st-15th characteristic structure can be exhibited.

  In addition, an energy supply evaluation system according to the present invention includes each unit of the energy supply evaluation system having any one of the first to fifteenth features and can be connected to a client terminal via an electric communication line. And when the first data, the second data, and the third data are input from the client terminal via the telecommunication line, the power usage amount estimating means and the input data based on the input data The heat usage estimation means estimates the power usage by day of the day and the heat usage by time of the day, and the first evaluation value calculation means calculates the estimated power usage and heat usage. An evaluation value for use cost evaluation to cover the amount in the first energy supply form is calculated, and the second evaluation value calculation means calculates the estimated power usage amount and heat usage amount in the second energy supply. An evaluation value for use cost evaluation to cover in the state is calculated, and each evaluation value calculated by the first evaluation value calculation means and the second evaluation value calculation means is sent to the client terminal via the telecommunication line. The sixteenth feature is that the transmission is possible.

  According to the sixteenth feature configuration of the system of the present invention, the energy evaluation system having the first to fifteenth feature configurations is configured by a server, and the server is accessed from a client terminal via an electric communication line. Thus, a configuration can be adopted in which arithmetic processing is performed on the server in a unified manner. For this reason, for example, when reviewing the regression equation used for calculation, it is possible to cope with only changing the program in the server without introducing a modified program for each client terminal. .

  According to the system of the present invention, the lifestyle or geographical information of the energy consumer is identified according to the input data, and the energy usage amount is determined by an arithmetic processing method according to the lifestyle or geographical information. Estimation can be performed. Due to differences in lifestyle and geographical differences, consumer behavior patterns may change, and this appears as a difference in energy consumption patterns, resulting in differences in lifestyle and geographic differences. Accordingly, it is possible to estimate the amount of energy used in consideration of the difference in the energy consumption pattern due to the difference in lifestyle and the geographical difference by performing the estimation by an appropriate calculation processing method by changing the calculation processing method accordingly.

  Hereinafter, an embodiment of an energy evaluation system according to the present invention (hereinafter referred to as “the present invention system” as appropriate) will be described with reference to the drawings.

  In the system of the present invention, a sales representative (operator) visits an energy consumer (for example, a general household) to which both an energy medium capable of generating electric power and heat and electric power are supplied from the outside, and a simple input operation is performed. In order to estimate the electric power demand and heat demand of each household on the spot and cover the demand, each of the case of using the first energy supply form described later and the case of using the second energy supply form described later In this case, a predetermined evaluation value for use cost evaluation in the case is estimated and presented to the home, and an optimum energy supply form is recommended.

<Description of concept>
First, after describing the first energy supply mode and the second energy supply mode described above, each embodiment of the system of the present invention will be described.

  FIG. 1 is a schematic configuration diagram showing a configuration example of each energy supply mode. FIG. 1A shows an example of the first energy supply mode, and FIG. 1B shows an example of the second energy supply mode. ing.

  The first energy supply form 10 shown in FIG. 1 (a) and the second energy supply form 20 shown in FIG. 1 (b) are supplied with power from the system power supply 11 in any form, and the city gas network. A house 13 to which city gas is supplied from 12 is assumed. And the house 13 is the structure which has each energy load element of the electric power load 14, the hot water supply load 15, and the other gas equipment 16.

  Among these, in the 1st energy supply form 10, all the electric power demand P3 of the electric power load 14 is covered with the electric power P2 supplied from the external system | strain power supply 11. FIG. Further, the hot water supply load 15 which is one of the heat demands is covered by the gas water heater 17 that receives the city gas supplied from the city gas network 12. The city gas supplied from the city gas network 12 is supplied to another gas appliance 16 (for example, a gas table stove) in addition to being supplied to the gas water heater 17.

  On the other hand, in the second energy supply form 20, a combined heat and power system 21 is provided in the house 13, and the combined heat and power system 21 receives both city gas supply from the city gas network 12 and generates both electric power and heat. The power demand P3 of the power load 14 is covered by both the power P1 generated by the combined heat and power system 21 and the power P2 supplied from the system power supply 11.

  That is, the electric power P1 generated by the combined heat and power system 21 is connected to the electric power P2 supplied from the system power supply 11 and supplied to the electric power load 14 used in the house 13. Since the thermoelectric supply system (power generation system) 21 is usually installed outdoors, the outdoors are also included in the house 13.

  On the other hand, the exhaust heat of the combined heat and power system 21 heats water, which is a heat medium, via a heat exchanger 24, and the heated water (hot water) is stored in a hot water storage tank 23, which is a kind of heat storage means. Is supplied to a hot water supply load 15 such as a hot water supply currant, a bathtub, a thermal terminal device such as floor heating. The hot water stored in the hot water storage tank 23 is heated by the boiler 22 that uses city gas as fuel when the combined heat and power supply system 21 is below the set temperature when the combined heat and power supply system 21 is stopped. In the case where there is a heat demand (hot water supply demand) equal to or higher than the heat energy generated by the combined supply system 21, the boiler 22 is configured to operate. Furthermore, the electric heater 25 is provided in the hot water storage tank 23, and when the surplus electric power is generated in the electric power P1 generated by the cogeneration system 21, the electric heater 25 is operated to heat the hot water in the hot water storage tank 23. It has become.

  Here, the electric power P3 consumed by the electric power load 14 and the heat consumed by the hot water supply load 15 correspond to the electric power demand and the heat demand of the energy consumer, respectively.

  That is, the present invention covers the case where power demand and heat demand are covered by the first energy supply form as shown in FIG. 1 (a), and power demand by the second energy supply form as shown in FIG. 1 (b). In addition, in each of the cases where the heat demand is covered, a predetermined evaluation value for use cost evaluation is estimated by a method described later, and the costs can be compared between the two supply forms.

  In the following embodiments, city gas is assumed as the energy medium, and electric power and heat usage charges (total of gas charges and electricity charges) are used as the predetermined evaluation values.

<First Embodiment>
Hereinafter, a first embodiment of the system of the present invention (hereinafter referred to as “this embodiment” as appropriate) will be described with reference to FIGS.

  FIG. 2 is a schematic configuration diagram showing the configuration of the system 1 of the present invention in the present embodiment. As shown in FIG. 2, the system 1 of the present invention includes a power usage amount estimation unit 2, a heat usage amount estimation unit 3, a first evaluation value calculation unit 4, and a second evaluation value calculation unit 5. The second evaluation value calculation means 5 is provided with a combined heat and power pattern calculation means 6. Each of the above means 2 to 5 is a computer hardware resource (CPU and various storage devices) when the system 1 of the present invention is constituted by a portable general-purpose computer such as a notebook personal computer or PDA (Personal Digital Assistants). Etc.) and software resources (OS, various drivers, database management software, etc.) and functional means realized by software processing.

  Below, the fundamental function and operation | movement of each means 2-5 which comprise this invention system 1 are demonstrated.

  The power usage amount estimation means 2 performs a power usage amount estimation process to be described later based on the input first data and third data, and for example, power by time period in a day as shown in FIG. Estimate usage. On the other hand, the heat usage amount estimation means 3 performs a heat usage amount estimation process, which will be described later, based on the input second data and third data, for example, a time zone in one day as shown in FIG. Estimate the amount of heat used separately. The first to third data will be described later separately.

  FIG. 3 is an example of an estimation result, FIG. 3 (a) is a graph of an example of estimated power usage by time period estimated by the power usage estimation means 2, and FIG. 3 (b) is a heat usage estimation means. 3 is a graph of an example of estimated heat usage by time zone estimated by 3. FIG. According to this example, the power demand shows the maximum value at 20:00 at night, and the heat demand shows the maximum value at 21:00 at night. It is recognized that the heat demand is particularly large for 3 hours from 21:00 to 23:00, and that the other time periods are significantly smaller than the 3 hours. In FIG. 3 (b), the same Wh as the power consumption in FIG. 3 (a) is used as the unit of heat usage.

  The first evaluation value calculation means 4 calculates the total power of the day from the power usage by time zone estimated by the power usage estimation means 2 and the heat usage for the day estimated by the heat usage estimation means 3. Calculate the usage amount and the total heat usage amount, further calculate the required gas capacity from the total daily heat usage amount, integrate the power unit price and the gas unit cost stored in advance in the storage area of the system 1 of the present invention, Is added to obtain the usage charges for electric power and heat in the first energy supply mode.

Here, it is assumed that the unit price of electricity is 24 yen per kWh, the unit price of gas is 111 yen per m ³ , the total heat consumption per day is 30000 Wh, the total power consumption per day is estimated to be 75 kWh, If the total heat usage per day is covered by gas, the total gas usage per day is 2.3m ³ (30000Wh × 3600 × 0.238 / 11000/1000), which is 1 in the first energy supply mode. Electricity and heat usage per day is 2055 yen (electricity charge = 75 kWh x 24 yen, gas charge = 2.3 m ³ x 111 yen). It is assumed that the city gas supplied from the city gas network 12 is 13A city gas supplied to a general household, and gas fuel with a rated calorie of 11000 kcal / m ³ is used and calculated as 1J = 0.238 cal. is doing.

  When the second evaluation value calculation means 5 calculates the electricity and heat usage charges in the second energy supply mode, the combined heat and power pattern calculation means 6 performs the estimated daily power consumption by time zone and heat usage. The operation plan of the combined heat and power system 21 is determined from the amount, and the amount of gas used for each time period is calculated. The operation plan determination method will be described with reference to the processing flow illustrated in FIG. 4 and the estimation results of the hourly power consumption and the hourly heat consumption illustrated in FIG. 3.

  In the following description, for the sake of simplicity of explanation, it is assumed that the combined heat and power supply system 21 covers all of the heat usage by time period in one day shown in FIG.

  First, the time when the heat demand reaches the peak and the heat usage by time zone are obtained from the estimation result of the heat usage by time zone in one day (step S1). For example, according to the graph of FIG. 3A, the heat demand peaks at 21:00, and the heat usage by time zone at that time is 14000 Wh.

  Next, the scheduled heat storage time by the combined heat and power supply system 21, that is, the scheduled operation time is determined so that heat can be stored before the heat demand is generated (step S2). The combined heat and power system 21 generally selects a time zone in which the estimated power consumption is large since the energy saving effect is large when heat is stored when the amount of power generation is large. In FIG.3 (b), the operation | movement by the cogeneration system 21 is not yet planned, but it is before 21:00 which is the peak of the said heat demand, and it is estimated that there is much electric power consumption. Since it is 20 o'clock, the power supply by the combined heat and power system 21 is scheduled for 20:00.

  Here, if the power generation efficiency of the combined heat and power system 21 is 20%, the ratio between the power generation amount and the heat generation amount is 1: 4. When a gas engine power generation system with a rated output of 1 kW is used as the combined heat and power system 21, the calorific value per hour is 4000 Wh. That is, it is planned to store 4000 Wh at 20:00. Further, similarly, an operation plan is made in the order of 9 o'clock, 19 o'clock, and 8 o'clock in which the power demand is large, and when the heat storage plan amount becomes 16000 Wh and the heat demand amount of 14000 Wh at 20:00 is satisfied, step S2 is ended.

  Then, the total of the daily heat storage amount estimated by the combined heat and power system 21 satisfies the estimated daily heat use amount, or the estimated daily power consumption and the combined heat and power system 21 Steps S1 and S2 are repeated until the difference in the power generation plan amount falls below a predetermined value, for example, 700 W (step S3). At this time, an operation plan for another time zone excluding the time zone for which the operation plan has already been made is performed.

  In this way, after the operation plan of the combined heat and power system 21 is determined, the second evaluation value calculation means 5 is determined by the total daily power usage estimated by the power usage estimation means 2 and the operation plan. The electricity price is calculated by multiplying the difference in the power generation plan amount by the combined heat and power system 21 by the unit price of electric power, and the amount of gas used for the operation of the combined heat and power system 21 is obtained from the operation plan by the combined heat and power system 21 and multiplied by the gas unit price Calculate gas charges. And the usage fee of the electric power and heat | fever in a 2nd energy supply form is calculated | required by adding both.

Here, 24 yen per 1kWh electricity unit price, the gas unit price and 111 yen per 1 m ^3, the total heat consumption per day When 30000Wh, total power consumption per day was estimated to 75KWh, 1 day The power generation plan amount per unit of heat and power supply system 21 is 8000 Wh (the heat storage plan amount is 8000 × 4 = 32000 Wh), and the daily gas amount required for the operation plan of the heat and power supply system 21 is 3.1 m ³ (8000 Wh × 5 × 3600) × 0.238 / 11000/1000), so the daily energy and heat usage charge in the second energy supply form is 1952 yen (electricity charge = 67 kWh × 24 yen, gas charge = 3.1 m ³ × 111 yen) Become.

  From the usage charges for power and heat per day in the first and second energy supply modes calculated as described above by the first evaluation value calculation means 4 and the second evaluation value calculation means 5, for example, one month The difference between the electric power and heat usage charges in the first and second energy supply forms is obtained and displayed on the computer on which the system 1 of the present invention is mounted, thereby using the power and heat in the second energy supply form. When the charge is cheaper than the usage charge according to the first energy supply mode, the operator introduces the combined heat and power supply system 21 to a general household who is an energy consumer, thereby switching to the second energy supply mode. Migration can be recommended.

  In the above specific example, all heat supply for heat use is covered by heat stored by the combined heat and power supply system 21, but only a part of the heat demand, for example, hot water supply demand can be supplied by the combined heat and power supply system 21. In this case, an operation plan is made only for the hot water supply demand, and for other heat demands, the city gas rate may be calculated as in the first energy supply mode.

  Next, each data used in the system 1 of the present invention will be described.

  As shown in FIG. 2, the system 1 of the present invention performs calculation processing using the first to third types of data, and thereby uses electricity and gas according to the first and second energy supply modes. Is calculated.

The first data is information regarding a household (for example, a general household, hereinafter referred to as “target household”) that is a target for cost comparison according to the first and second energy supply modes, and is information regarding a family structure. Family composition information (number of families N (people)), daytime home presence / absence information (existing at home: X = 1, no home away from home: X = 0), and floor area of residence It consists of floor area information (S (m ² )) that is information, and the actual value (PP (Wh)) of the accumulated power consumption during a specific period. The specific period represents a period to which the power consumption amount and the heat usage amount are estimated when the year is divided when the year is divided into 12 periods per month. .

  The second data is configured by the actual value (PG (Wh)) of the accumulated heat usage amount in a specific period in addition to the family configuration information, daytime resident presence / absence information, and floor area information constituting the first data.

  The third data is composed of geographical information that is geographical information of the target household. For example, the country name and the prefecture name may be configured to be identifiable, and when the operator inputs, the corresponding information may be selected from a pull-down list.

  In the case of ordinary households that contract electric power and gas, each household is generally assigned a unique contract number. For example, when the operator is an employee of a gas company supplying city gas to the target household, the gas company can recognize the address (geographic information) from the contract number. A configuration may be adopted in which a city gas contract number is input as data. In this case, the computer in which the system 1 of the present invention is installed may be provided with a conversion program for converting the contract number into geographical information. As will be described later in the second embodiment, the computer performs telecommunications. In the case of a portable general-purpose computer that can be connected to a line, it may be configured to connect to a gas company host terminal (server) and inquire geographical information from a contract number.

  In particular, the third data is input in order to make the system 1 of the present invention recognize the geographical information of the target household in order to take into account lifestyle differences between regions when estimating the power consumption and the heat usage. Therefore, detailed geographical information that does not require consideration of lifestyle changes is unnecessary. That is, for example, as shown in FIG. 18 and FIG. 19, it may be composed of information on regions such as Hokkaido and Kinki, or information on temperature (for example, annual average temperature) that is one of the factors affecting the lifestyle. Information, maximum temperature information, minimum temperature information, etc.). Further, in the above contract number, for example, when the first two digits represent information related to the administrative district, only the two digits may be input.

  When the operator inputs the first to third data to the computer, as described above, the first data and the second data have common data (hereinafter referred to as “common data”). The data that has been entered once is used in common. In addition, information on the actual values related to power and gas in a specific period is stored in a storage area of the computer as a plurality of pre-patterned sample information, and one sample is selected from the plurality of samples according to the input common data. By selecting, it is possible to easily estimate the power usage and heat usage with high reliability that are consistent with past performance values.

  At this time, the configuration may be such that the actual value sample is selected in consideration of the third data in addition to the common data, and correction is performed by performing a predetermined calculation on the actual value sample according to the third data. It is good also as an electric power and gas usage-amount actual value in the specific period of the said object household. In the following, the actual value sample does not depend on the third data, but uses sample data that depends only on the first data and the second data, and corrections due to geographical differences are performed within the arithmetic processing as will be described later. Shall be considered.

  An operator inputs the common data and the third data to a computer on which the system 1 of the present invention is mounted by using an input device such as a keyboard or a tablet, and the actual value sample is called from the storage area. By performing predetermined arithmetic processing, the first data and the second data are configured from the input data. In the following description, the third data will be described with an appropriate symbol “G”.

  Next, the power usage amount estimation process for estimating the power usage amount for each time period of the day performed by the power usage amount estimation means 2 will be described.

  FIG. 5 is a block diagram showing the configuration of the power usage amount estimation means 2. As shown in FIG. 5, the power usage amount estimation unit 2 includes time-based usage amount estimation unit 41, usage amount pattern estimation unit 42, period power usage amount calculation unit 43, cumulative power usage amount comparison unit 44, and power estimation. Correction means 45 and second usage pattern estimation means 46 are provided. The computer provided with the system 1 of the present invention also displays an input unit 31 for inputting information to the power usage estimation means 2, an input content from the input unit 31, and information provided from the power usage estimation means 2. It is assumed that the storage unit 33 in which predetermined information including information input from the unit 32 and the input unit 31 is stored is stored. At this time, the input unit 31 includes an input device such as a keyboard and a touch pad, the display unit 32 includes a display device such as a liquid crystal display, and the storage unit 33 includes a storage device such as a hard disk and a memory. The

  In addition to the power usage estimation means 2, the computer provided with the system 1 of the present invention includes a heat usage estimation means 3 described later. The input unit 31, the display unit 32, and the storage unit 33 include The following description will be made on the assumption that the components are common to both means.

  When the operator starts the system 1 of the present invention on the computer, the display unit 32 displays family configuration information (N), daytime presence / absence information (X), floor area information (S), and third data. A display requesting input of data (hereinafter referred to as “administrative district information (G)”) is made. When the operator inputs such information from the input unit 31, the input data is sent to the storage unit 33 and temporarily stored.

  As will be described later, in the heat usage estimation means 3, the inputted family composition information (N), daytime presence / absence information (X), floor area information (S), and administrative district information (G) are also stored. Used when performing arithmetic processing.

The usage amount estimation unit 41 by time zone reads the information N, X, S, G from the storage unit 33, and uses the energy usage amount by time zone in the first characteristic time zone in which the power usage amount is large using the information. The peak demand that is (power amount Wh) and the bottom demand that is the power usage amount (power amount Wh) for each time zone in the second characteristic time zone with a small amount of power usage are estimated. Here, in the system 1 of the present invention, one day is divided into 24 time zones every hour, and the first characteristic time zone is 2-3 hours after wake-up time, 2-3 hours after noon, 7 pm Three time zones t _{P1 to} t _P3 included in each time range of 2 to 3 hours before and after the hour are set, and the second characteristic time zone is 2 to 3 hours before the wake-up time, and about 1 to 3 am Three time zones t _{B1 to} t _B3 included in each time range of 2 to 3 hours before and after 3 pm are set.

FIG. 6 is a graph illustrating the relationship between the fluctuation pattern of the daily power consumption in a general home and the respective time zones t _{P1 to} t _P3 and t _{B1 to} t _B3 . As shown in FIG. 6, three peak demands (three peak demands in the morning, noon, and night) and three bottom demands (early morning, morning, and day three bottom demands) occur in a general household. Therefore, three time zones are set as the first and second feature time zones, respectively.

Here, as a daily life pattern of a standard home that is a setting standard of the first characteristic time zones t _{P1 to} t _P3 and the second characteristic time zones t _{B1 to} t _B3 , a family member is 6 am It is assumed that a person who gets up around 7:00 am, goes out for breakfast, goes home in the daytime, has lunch after noon, and goes out for dinner in the evening.

Assuming that the estimated values of the power usage (Wh) by time zone in each time zone t _{P1 to} t _P3 and t _{B1 to} t _B3 are E _{P1 to} E _P3 and E _{B1 to} E _B3 , respectively, (Wh) is given by the following regression equation. Here, it is assumed that the bottom demands E _B2 and E _B3 in the morning and noon are substantially equal.

(Equation 1)
E _P1 = i _EPS1 × S + i _EPN1 × N + i _EPX1 × X + C _EP1
E _P2 = i _EPS2 × S + i _EPN2 × N + i _EPX2 × X + C _EP2
E _P3 = i _EPS3 × S + i _EPN3 × N + i _EPX3 × X + C _EP3
E _B1 = i _EBS1 × S + i _EBN1 × N + i _EBX1 × X + C _EB1
E _B2 = E _B3 = i _EBS2 × S + i _EBN2 × N + i _EBX2 × X + C _EB2

Note that each regression equation coefficients and constants (hereinafter, referred to as "first coefficient group") _i EPS 1 _{is, i EPN1, i EPX1, C} EP1, i EPS2, i EPN2, i EPX2, C EP2, i EPS3, _{i EPN3, i EPX3, C EP3} , i EBS1, i EPN1, i EBX1, C EB1, i EBS2, i EPN2, i EBX2, s values husband _{C EB2} is uniquely determined by administrative district information G. At this time, the storage unit 33 may store each value of the first coefficient group corresponding to the administrative district information G as a data table.

At this time, each coefficient value which comprises the 1st coefficient group corresponding to the value of administrative district information G is read from the memory | storage part 33, and by substituting into the said Formula 1, electric power consumption amount _EP1 classified by estimated time slot | zone. E _P3 and E _{B1 to} E _B3 are calculated. For example, when the value of the information G indicates information on Osaka Prefecture, the coefficients constituting the first coefficient group are i _EPS1 = 6.58, i _EPN1 = 207, i _EPX1 = 0, C _EP1 = −896, i, respectively. _{EPS2 = 4.13, i EPN2 = 186} , i EPX2 = 154, C EP2 = -850, i EPS3 = 7.48, i EPN3 = 171, i EPX3 = 0, C EP3 = -680, i EBS1 = 1.2, i EPN1 = 11.2 , I _EBX1 = 0, C _EB1 = 146, i _EBS2 = 8.48, i _EBN2 = 147, i _EBX2 = 115, and C _EB2 = -1152. Under this factor, if S = 100 (m ³ ), N = 4 (people), and there are people staying in the daytime (X = 1), the peak demand and the bottom demand are E _P1 = 592 Wh and E _P2 = 464 Wh, E _P3 = 754 Wh, E _B1 = 311 Wh, E _B2 = E _B3 = 401 Wh.

  Further, as described above, the system 1 of the present invention divides one year into twelve periods for each month, and defines a period to which one day as an estimation target of power demand belongs as a specific period. Therefore, the regression equation of Equation 1 has various family structures, whether there are people staying in the daytime, floors in the intermediate period (for example, May and October) when air conditioning equipment such as heating and cooling is not used as the specific period. A regression equation derived by a general method is used based on measured data of power demand in a large number of family members. In this way, by using the regression formula of the intermediate period, it is possible to estimate with high accuracy while suppressing the influence of the variation in the amount of power used due to air conditioning. Furthermore, the lifestyle difference can be taken into account by changing the coefficient of the regression equation according to the region.

  Note that the day on which power demand is estimated is virtually set to the intermediate period, and the time when the sales representative actually visits each home does not necessarily have to be the intermediate period.

The estimated values E _{P1 to} E _P3 and E _{B1 to} E _B3 estimated by the usage amount estimation unit 41 for each time zone are sent to the usage pattern estimation unit 42. The usage pattern estimator 42 is based on the power usage amounts E _{P1 to} E _P3 and E _{B1 to} E _B3 , which are estimated by the hourly usage amount estimation unit 41, for one day to be estimated for power demand. Interpolate the power usage by time zone in all the time zones in the following manner between the power usage by time zone in two adjacent first and second characteristic time zones t _{P1 to} t _P3 and t _{B1 to} t _B3 Then, the fluctuation pattern of the daily power consumption is estimated.

FIG. 7 is a conceptual diagram for explaining the interpolation processing performed by the usage pattern estimating means 42, and takes up the time period from the time t _{P3 at} night peak demand to the time t _B1 at early morning bottom demand. As shown in FIG. 7, the interpolation from the first characteristic time zone t _{P3 at} night to the second characteristic time zone t _{B1 in the} early morning is performed by calculating the difference ΔE ₁ between the power usage amounts E _P3 and E _B1 by time zone as follows: The power usage amount for each time zone in each intermediate time zone is calculated by allocating to each intermediate time zone at the ratio. That is, 5% of the difference ΔE ₁ after 1 hour from the first characteristic time zone t _P3 , 25% of the difference ΔE ₁ after 2 hours, 35% of the difference ΔE ₁ after 3 hours, 20% of the difference ΔE ₁ after 4 hours, Thereafter, 15% of the remaining difference ΔE ₁ is evenly distributed until the second feature time zone t _B1 .

For other time zones, interpolation processing is performed in the same manner. For example, the interpolation from the second characteristic time zone t _B1 in the early morning to the first characteristic time zone t _{P1 in the} morning includes the difference ΔE ₂ between the power consumption amounts E _B1 and E _P1 for each time zone at the following ratios. The power usage amount by time zone in each intermediate time zone is calculated by allocating to each time zone. That is, 80% of the difference Delta] E ₂ to 1 hour before the first characteristic time period _{t P1,} from previous second characteristic time period _{t B1} until 1 hour before the first characteristic time period _{t P1,} the remaining difference Delta] E Distribute 20% of ₂ evenly.

Further, the interpolation from the first characteristic time zone t _{P1 in the} morning to the second characteristic time zone t _{B2 in the} morning is performed by setting the difference ΔE ₃ between the energy usage amounts E _P1 and E _B2 for each time zone at the following ratios. The power usage amount by time zone in each intermediate time zone is calculated by allocating to each time zone. That is, 0% of the difference ΔE ₃ after 1 hour from the first feature time zone t _P1 (this means that the first feature time zone t _P1 is 2 hours), and after 80 hours of the difference ΔE ₃ %, 20% of the difference ΔE ₃ is allocated after 3 hours.

The interpolation from the second characteristic time zone t _B2 in the morning to the first characteristic time zone t _{P2 in the} daytime includes the difference ΔE ₄ between the power usage amounts E _B2 and E _{P2 according to} the time zones at the following ratios. The power usage amount by time zone in each intermediate time zone is calculated by allocating to each time zone. That is, 1 hour before 90% of the difference Delta] E ₄ of the first characteristic time period _{t P2,} to allocate 10% of the difference Delta] E ₄ 2 hours ago.

Further, the interpolation from noon of the first characteristic time period _{t P2} to the second characteristic time period _{t B3} of day, the difference Delta] E ₅ of hourly power usage _{E P2} and _{E B3,} in the following proportions, each intermediate The power usage amount by time zone in each intermediate time zone is calculated by allocating to each time zone. That is, 90% of the difference Delta] E ₅ from the first characteristic time period _{t P2} after one hour, to allocate 10% of the difference Delta] E ₅ after 2 hours. As a result, if there is a daytime resident and there is a daytime peak demand E _P2 , the daytime first characteristic time zone t _P2 is from the morning second characteristic time zone t _B2 to the daytime second characteristic time zone t _B3. It becomes a symmetrical chevron pattern across.

Further, the interpolation from the second characteristic time zone t _{B3 in the} day to the first characteristic time zone t _{P3 in the} night is performed by setting the difference ΔE ₆ between the power consumption amounts E _B3 and E _{P3 according to} the time zone at the following ratios. The power usage amount by time zone in each intermediate time zone is calculated by allocating to each time zone. That is, 60% of the difference Delta] E ₆ to 1 hour prior to the first characteristic time period _{t P3,} 25% of the difference Delta] E ₆ to 2 hours prior to allocate 15% of the difference Delta] E ₆ before 3 hours.

  By the above interpolation process, the fluctuation pattern of the power usage amount in all time zones of the day is obtained.

  In addition, when performing the said interpolation process, it is good also as a structure which changes according to the value of the administrative district information G about the value of the allocation rate which allocates a time slot | zone. In this case, the value of the distribution rate according to the value of the administrative district information G is stored as a data table in the storage unit 33, and the corresponding allocation rate value according to the administrative district information G given from the input unit 31 is stored in the storage unit. A configuration may be adopted in which the usage pattern estimation unit 42 is provided from 33.

  When the usage pattern estimation unit 42 calculates the fluctuation pattern of the power usage over the entire time period, the calculation result is sent to the period power usage calculation unit 43. The period power usage calculation means 43 calculates the specific period from the power usage amount for each time zone given by the usage pattern estimation means 42, that is, the fluctuation pattern of the power usage in all time zones of the day. Calculate the accumulated power usage. The calculation method is simply to find the sum of 24 hourly power usages and multiply by the number of days in a specific period.

  The accumulated power usage for a specific period calculated by the period power usage calculating means 43 is sent to the accumulated power usage comparing means 44. The accumulated power usage comparison unit 44 stores the past actual value of the accumulated electricity usage for a specific period of the energy consumer stored in the storage unit 33 and the accumulated power usage calculated by the period electricity usage calculation unit 43. And the error is calculated. Then, the calculated error is sent to the display unit 32, and the display unit 32 can visually confirm the error.

  As a display method at this time, for example, the past actual value and the estimated value of the accumulated power usage may be displayed side by side, or the past actual value, the estimated value, and the error rate (= (Error / actual value × 100%) may be displayed side by side. Here, together with the error display, the estimation result of the fluctuation pattern of the daily power consumption as shown in FIG. 6 may be displayed on the display unit 32 as a graph.

The accumulated power usage comparison unit 44 further provides the comparison result to the power estimation correction unit 45. The power estimation correcting unit 45 determines whether or not the error value is within a predetermined range (for example, within ± 5%). If the error value is larger than the range, the display unit 32 displays the error value. Display the correction input screen. That is, on the display unit 32, one or all of the first characteristic time zones t _{P1 to} t _P3 and the second characteristic time zones t _{B1 to} t _B3 are displayed so as to be selectable. The correction amount is displayed so as to be input for each feature time zone.

When the correction input screen is displayed on the display unit 32, the operator can select one or all of the first characteristic time zones t _{P1 to} t _P3 and the second characteristic time zones t _{B1 to} t _B3. And an appropriate correction amount estimated from the displayed error is input for each of the selected feature time zones. As the input of the correction amount, an increase / decrease rate (correction amount / estimated energy usage per time zone × 100%) with respect to the estimated power usage per time zone may be input.

In the system 1 of the present invention, as an index of the degree of variation in power consumption by time zone in the first characteristic time zones t _{P1 to} t _P3 and the second characteristic time zones t _{B1 to} t _B3 , The standard deviations of the power usage data for each time zone in each of the intermediate period, summer period, and winter period are obtained and stored in the storage unit 33. The power estimation correction means 45 first reads the value of the standard deviation of the intermediate period from the storage unit 33, and, according to the ratio of the standard deviation value, from the correction amount in the specified time zone, other unspecified first A correction amount of the power usage amount for each time zone between the feature time zone and the second feature time zone is calculated. For example, the ratio of the correction amount based on the standard deviation in the first characteristic time zone t _{P1 to} t _P3 and the second characteristic time zone t _{B1 to} t _B3 is 0.27, 0.14, 0.28, 0.14, 0.17, 0.17, respectively. If the first characteristic time zone _tP2 is corrected with the specified correction amount, the correction amount of the first characteristic time zone _tP3 is doubled, and the second characteristic time zone _tB1 is corrected. The amount will be the same.

  The power estimation correction means 45 may display the correction amount ratio (% display) on the display unit 32 as a reference value of the increase / decrease ratio (% display) when displaying the correction input screen.

  The power estimation correcting means 45 accepts the correction of the power usage by time zone in one time zone, calculates the correction amount of the power usage by time zone in the remaining five time zones, and the power by time zone after these corrections The usage amount is input to the usage amount pattern estimating means 42, and the daily power usage amount fluctuation pattern estimation process (interpolation process) is executed again. Next, the period power usage calculating unit 43, the cumulative power usage comparing unit 44, and the power estimation correcting unit 45 calculate an intermediate estimation result again in the same manner as the initial processing, and the intermediate estimation result is the estimation. It is displayed on the display unit 32 together with the error. The series of processes is repeated until the estimation error falls within a certain minute range (for example, within ± 5%).

When the estimation error falls within the certain small range, the second usage pattern estimation unit 46 is activated, and the same estimation process as in the intermediate period is executed for periods other than the intermediate period. When the estimation target period is winter, the second usage pattern estimator 46 uses the power consumptions E _{P1 to} E _P3 and E _{B1 to} E _B for each intermediate period estimated by the hourly usage estimation unit 41. Temperature correction amounts A _{P1 to} A _P3 and A _{B1 to} A _B3 (Wh / ° C.) with respect to _B3 are derived using the following regression formula 2. S, N, and X in Equation 2 are the same as the first data in Equation 1. In addition, since the heating amount increases as the temperature difference increases, the temperature correction amount increases.

(Equation 2)
_{A P1 = i APS1 × S +} i APN1 × N + C AP1
A _P2 = (i _APS2 × S + i _APN2 × N + C _AP2 ) × X
A _P3 = i _APS3 × S + i _APN3 × N + C _AP3
A _B1 = i _ABS1 × S + i _ABN1 × N + C _AB1
A _B2 = A _B3 = (i _ABS2 × S + i _ABN2 × N + C _AB2 ) × X

Incidentally, the coefficient of each regression equation of the equation in 2 and the constant (hereinafter, referred to as "second coefficient group") _i APS1 _{is, i APN1, C AP1, i} APS2, i APN2, C AP2, i APS3, i _{APN3, C AP3, i ABS1,} i ABN1, C AB1, i ABS2, i ABN2, the value of _{C AB2} each is uniquely determined by administrative district information G. At this time, the storage unit 33 may store each value of the second coefficient group corresponding to the administrative district information G as a data table.

In this case, the coefficient values constituting a second coefficient group corresponding to the value of the administrative district information G from the storage unit 33 is read out, that is substituted into Equation 2, the temperature correction amount A _{P1 to A P3,} A _B1 ~A _B3 is calculated. For example, if the value of the information G indicates the information of Osaka, second coefficients constituting the coefficient group respectively _{i APS1 = 1.423, i APN1 =} 0.672, C AP1 = 1.068, i APS2 = 0.439, i APN2 _{= 0.206, C AP2 = 0.827,} i APS3 = 1.025, i APN3 = 0.481, C AP3 = 0.765, i ABS1 = 0.163, i ABN1 = 0.076, C AB1 = 0.122, i ABS2 = 0.373, i ABN2 = 0.185, C AB2 = 0.279 is given from the storage unit 33. Under this coefficient, assuming that S = 100 (m ³ ), N = 4 (people), and there are people staying in the daytime (X = 1), the respective temperature correction amounts A _{P1 to} A _P3 and A _{B1 to} A _B3 are respectively obtained. A _P1 = 146.056, A _P2 = 48.765, A _P3 = 105.189, A _B1 = 16.726, and A _B2 = A _B3 = 38.319.

The second usage pattern estimating unit 46 reads the average temperature (for example, 15 ° C.) for the specific period and the average temperature for the estimation target period (winter) from the storage unit 33, and calculates the temperature difference between the temperature correction amounts A _P1 to A _P1 . A value obtained by multiplying A _P3 and A _{B1 to} A _B3 is calculated. The calculated value is, the correction amount for each time zone power usage interim _{E P1 ~E P3, E B1 ~E} B3 ΔE P1 ~ΔE P3, the ΔE _B1 ~ΔE _B3.

In Equation 2, when the first input data is S = 100 (m ³ ), N = 4 (person), there are people staying in the daytime (X = 1), and the average temperature of the estimation target period (winter) is 11 ° C. The correction amount (increase amount) of each peak demand and bottom demand is ΔE _P1 = 588 Wh, ΔE _P2 = 180 Wh, ΔE _P3 = 421 Wh, ΔE _B1 = 67 Wh, ΔE _B2 = ΔE _B3 = 158 Wh, respectively.

The same applies when the estimation target period is summer. Temperature correction amounts B _{P1 to} B _P3 and B _{B1 to} B _B3 for the summer period with respect to the electric power consumption amounts E _{P1 to} E _P3 and E _{B1 to} E _B3 for each intermediate period estimated by the hourly usage amount estimation means 43 (Wh / ° C.) is derived using the following regression equation (3). S, N, and X in Equation 3 are the same as the first input data in Equation 1. The temperature correction amount increases as the temperature difference increases, so the correction amount increases.

(Equation 3)
B _P1 = i _BPS1 × S + i _BPN1 × N + C _{BP1
B P2 = (i BPS2 × S} + i BPN2 × N + C BP2) × X
B _P3 = i _BPS3 × S + i _BPN3 × N + C _BP3
B _B1 = i _BBS1 × S + i _BBN1 × N + C _BB1
B _B2 = B _B3 = (i _BBS2 × S + i _BBN2 × N + C _BB2 ) × X

Note that each regression equation coefficients and constants of the equation 3 (hereinafter, referred to as "third coefficient group") in which _{i BPS1, i BPN1, C BP1} , i BPS2, i BPN2, C BP2, i BPS3, i _{BPN3, C BP3, i BBS1,} i BBN1, C BB1, i BBS2, i BBN2, the value of _{C BB2} each is uniquely determined by administrative district information G. At this time, the storage unit 33 may store each value of the third coefficient group corresponding to the administrative district information G as a data table.

At this time, as in the case where the temperature correction amounts A _{P1 to} A _P3 and A _{B1 to} A _B3 are calculated, the coefficient values constituting the third coefficient group corresponding to the value of the administrative district information G are read from the storage unit 33. By substituting into the above equation ₃ , the temperature correction amounts B _{P1 to} B _P3 and B _{B1 to} B _B3 are calculated. For example, if the value of the information G indicates the information of Osaka, third coefficients constituting the coefficient group respectively _{i BPS1 = 2.24, i BPN1 =} 55.2, C BP1 = -813, i BPS2 = 27.1, i _{BPN2 = 668, C BP2 = -9841} , i BPS3 = 41, i BPN3 = 1008, C BP3 = -14860, i BBS1 = 4.82, i BBN1 = 119, C BB1 = -1749, i BBS2 = 21.3, i BBN2 = The storage unit 33 gives 525, C _BB2 = -7743. Under this coefficient, assuming that S = 100 (m ³ ), N = 4 (people), and there are people staying in the daytime (X = 1), the temperature correction amounts B _{P1 to} B _P3 and B _{B1 to} B _B3 are respectively obtained. , B _P1 = −368.2, A _P2 = −4459, A _P3 = −6728, A _B1 = −791, and A _B2 = A _B3 = −3513.

The second usage pattern estimator 46 reads the average temperature (for example, 15 ° C.) during the specific period and the average temperature during the estimation target period (summer) from the storage unit 32, and calculates the temperature difference between each of the temperature correction amounts B _P1 to B _P1 . A value obtained by multiplying B _P3 and B _{B1 to} B _B3 is calculated. The calculated value is, the correction amount for each time zone power usage interim _{E P1 ~E P3, E B1 ~E} B3 ΔE P1 ~ΔE P3, the ΔE _B1 ~ΔE _B3.

In Equation 3, when the first data is S = 100 (m ³ ), N = 4 (people), there are people staying in the daytime (X = 1), and the average temperature is 30 ° C. during the estimation target period (summer), The correction amounts (increases) of the peak demand and the bottom demand are ΔE _P1 = 50 Wh, ΔE _P2 = 610 Wh, ΔE _P3 = 921 Wh, ΔE _B1 = 108 Wh, and ΔE _B2 = ΔE _B3 = 480 Wh, respectively.

As described above, the correction amounts ΔE _{P1 to} ΔE _P3 and ΔE _{B1 to} ΔE _B3 derived in the above manner for the respective periods of the winter season and the summer season are used as the power consumption amounts E _{P1 to} E _P3 and E _B1 to E _B1 to the intermediate periods. By adding to E _B3 , the power usage by time period in each period is obtained.

The second usage pattern estimation unit 46 inputs the power usage by time period in each of the time zones t _{P1 to} t _P3 and t _{B1 to} t _B3 of the derived period other than the intermediate period to the usage pattern estimation unit 42. The usage pattern estimator 42, when given the value of the power usage by time period in a period other than the interim period from the second usage pattern estimator 46, uses the same interpolation formula as that for the interim period, Calculate the power usage by time zone. Thereby, the fluctuation pattern of the electric power consumption in all the time zones in the entire period of one year is obtained.

  In addition, in the above, the standard deviation value in each of the intermediate period, summer period, and winter period of the power consumption data for each time period in each time zone stored in advance in the storage unit 33 is the administrative district information G A configuration may be adopted in which each value is stored as an individual value. In this case, each standard deviation value corresponding to the value of the administrative district information G is stored as a data table in the storage unit 33, and the corresponding standard deviation value corresponding to the administrative district information G given from the input unit 31 is the power. A configuration may be adopted in which the correction amount of another feature time zone is determined in accordance with the correction amount in the feature time zone which is given to the estimation correction means 45 and is input based on the standard deviation value.

In the above description, the bottom demands E _B2 and E _B3 in the morning and noon are substantially equal. However, the power amount estimation processing may be performed with these values different from each other. In this case, a regression equation for _obtaining E _B2 and E _B3 in Equation 1, a regression equation for obtaining A _B2 and A _B3 in Equation _2, and a regression equation for _obtaining B _B2 and B _B3 in Equation 3, respectively. The storage unit 33 may include a data table for determining the values of these coefficients according to the administrative district information G.

  Each calculated value obtained by the power usage estimation process performed by the power usage estimation unit 2 and the heat usage estimation process performed by the heat usage estimation unit 3 described later is given to the storage unit 33. It is assumed that the configuration is appropriately held. The same applies to the following.

  Next, the heat usage estimation process performed by the heat usage estimation means 3 for estimating the daily heat usage by time zone based on predetermined second data will be described.

  FIG. 8 is a block diagram showing the configuration of the heat usage estimation means 3. As shown in FIG. 8, the heat usage estimation means 3 includes a kitchen heat usage estimation means 51, a hot water supply heat usage estimation means 52, and a heating heat usage estimation means 53.

  In the following, the operator has already input the family composition information N, daytime presence / absence information X, floor area information S, and administrative district information G from the input unit 31, and the input information is stored in the storage unit 33. The description will be made assuming that the data is stored in the.

Kitchen heat amount estimating means 51 reads the family structure information N from the storage unit 33, estimates the total heat amount L _K according to the kitchen of the day by using the information. At this time, since city gas is assumed as an energy medium in this embodiment, the total heat usage L _K (Wh) related to the daily kitchen depends on the family configuration, for example, the presence or absence of a gas rice cooker, and the like. It can be calculated using the estimated usage amount G _K (m ³ ) of the city gas (energy medium) related to the kitchen for one day. The value of the gas cooker presence / absence information Z (gas cooker present: Z = 1, gas cooker absent: Z = 0) is input by the operator separately from the input unit 31.

The total heat usage L _K related to the daily kitchen is given by the following equation (4).

(Equation 4)
L _K = G _K × 11000 × 1000 / 0.238 / 3600
G _K = (i _GZ × N + i _GZ × Z + C _G1 ) / C _G2

Note that the values of i _GZ , i _GZ , C _G1 , and C _G2 that are coefficients and constants (hereinafter referred to as “fourth coefficient group”) of the regression equation are uniquely determined by the administrative district information G. At this time, the storage unit 33 may store each value of the fourth coefficient group corresponding to the administrative district information G as a data table.

At this time, each coefficient value constituting the fourth coefficient group corresponding to the value of the administrative district information G is read from the storage unit 33 and is substituted into the above equation 4, so that the estimated total heat usage related to the kitchen for one day the amount _{L K} is calculated. For example, when the value of the information G indicates information of Osaka Prefecture, the coefficients constituting the fourth coefficient group may be i _GZ = 0.93, i _GZ = 1.0, C _G1 = 2.7, and C _G2 = 30. It is given from the storage unit 33. If N = 4 (person) and a gas cooker is present (Z = 1) under this factor, L _K = 3175 Wh.

  In addition, the regression equation of Equation 4 is also the same as the regression equation of Equation 1, and the specific period is an intermediate period such as May or October, in which no air conditioning equipment such as heating or cooling is used, and various families A regression equation derived by a general method is used based on actual measurement data of heat demand in the configuration.

The hot water supply heat usage estimation means 52, when the kitchen heat usage estimation means 51 derives the total heat usage L _K (Wh) related to the daily kitchen, calculates the total heat usage L _K related to the daily kitchen. The total heat usage L _W (Wh) related to the hot water supply for one day is estimated. Here, the amount actual value of the gas of the day in the interim period (energy medium) and G _{M (m} ^3), but the heating heat consumption by the use of air-conditioning equipment in the period, further, the daily kitchen From the empirical rule that the total heat consumption L _K related to the daily heat supply is almost constant throughout the year, the total heat usage L _W related to the daily hot water supply in the interim period is calculated as follows. ) Using the actual usage amount G _M (m ³ ), and calculating the difference from the total heat usage L _K (Wh) related to the kitchen.

(Equation 5)
_{L W = G M × 11000 ×} 1000 / 0.238 / 3600-L K

In Equation 5, if the estimated total heat usage for the daily kitchen is L _K = 3175 Wh, and the actual usage amount of gas (energy medium) in the interim period is G _M = 2.3 m ³ , The total amount of heat used for hot water supply during the season is L _W = 26.4 kWh.

Furthermore, the total heat amount L _W is according to the hot water supply, for the period other than the period, the total heat consumption of the hot water per day in the interim period derived by Equation 5 and L _WM, monthly average temperature interim Is P _M (° C.), and the average temperature of the month to which the estimation target period belongs is P (° C.).

(Equation 6)
L _W = L _WM × (C _W1 −P) / (C _W2 −P _M )

The values of C _W1 and C _W2 , which are coefficients and constants (hereinafter referred to as “fifth coefficient group”) of the estimation formula shown in Equation 6, are uniquely determined by administrative district information G. At this time, the storage unit 33 may store each value of the fifth coefficient group corresponding to the administrative district information G as a data table.

At this time, each coefficient value which comprises the 5th coefficient group corresponding to the value of the administrative district information G is read from the memory | storage part 33, and the total heat usage amount which concerns on the hot water supply of 1 day by substituting into the said Formula 6 _Lw is calculated. For example, when the value of the information G indicates information on Osaka Prefecture, the storage unit 33 gives that the coefficients constituting the fifth coefficient group are C _W1 = 33 and C _W2 = 33. Here, the total amount of heat used for hot water supply during the intermediate period is L _WM = 26.4 kWh, the monthly average temperature in the intermediate period is P _M = 15 (° C), and the average temperature in the estimation target period (winter) is P = 11 (° C.), the total heat consumption for hot water supply is L _W = 32.3 kWh. Further, if the average temperature during the estimation target period (summer season) is P = 30 (° C.), the total heat consumption for hot water supply is L _W = 4.4 kWh.

  In addition, since the fluctuation pattern according to the time zone of the day of the total heat consumption related to the kitchen and the total heat consumption related to hot water supply is almost common in general households, by using the standard fluctuation pattern, It is possible to estimate the amount of heat used by the time period of the day in the interim period. In other words, a standard variation pattern obtained from past performance data is registered in the storage unit 33 in advance, and the total heat usage related to the daily kitchen in the intermediate period estimated by the kitchen heat usage estimating means 51. By using the total heat usage related to the hot water supply in one day in the intermediate period estimated by the hot water supply heat usage estimation means 52, the heat usage estimation means 3 uses the heat usage by time period in the intermediate period in one day Estimate the amount.

The heating heat usage estimation means 53 calculates the total heat usage related to the heating of the day, so that the power usage estimation means 2 estimates the power usage for each time period during the winter season estimated using the regression equation (2). The amount E _HJ (where J = P1 to P3, B1 to B3, and so on) is read from the storage unit 33, and as shown in the following Equation 7, the amount of electric power used for each time zone in this winter season and the energy of the electrical equipment From the consumption efficiency, the total heat usage L _HJ (Wh) related to heating for one day is calculated.

(Equation 7)
L _HJ = E _HJ × α
(However, α = Energy consumption efficiency of electrical equipment)

Here, when the common data is S = 100 (m ² ), N = 4 (person), there are people staying in the daytime (X = 1), and the average temperature is 11 ° C. in the specific target period (winter), each peak time The electric power consumption by time of the electrical equipment required for heating heat generation at the bottom time is E _HP1 = 588 Wh, E _HP2 = 180 Wh, E _HP3 = 421 Wh, E _HB1 = 67 Wh, E _HB2 = E _HB3 = 158 Wh, respectively. Furthermore, assuming that the energy efficiency of the electrical equipment is 4.0, the heat usage amount L _HJ for each heating time period for the day heating is L _HP1 = 2.35 kWh, L _HP2 = 720 Wh, L _HP3 = 1.68 kWh, L _HB1 = Since 268 Wh and L _HB2 = L _HB3 = 632 Wh, the total amount of heat used for one day heating is L _H = 5.66 kWh.

  As described above, the kitchen heat usage estimation means 51, the hot water supply heat usage estimation means 52, and the heating heat usage estimation means 53 can be used for one day kitchen, hot water supply, and heating in a specific period such as an intermediate period, winter season, and summer season. Each such total heat consumption is estimated.

  The heat usage estimation means 3 is based on the total daily heat usage estimated by the kitchen heat usage estimation means 51, the hot water supply heat usage estimation means 52, and the heating heat usage estimation means 53, respectively. Estimate heat usage by time period. The daily fluctuation pattern of kitchen heat consumption and hot water supply heat usage by time zone is almost common in ordinary households. Therefore, based on past performance data, etc., the daily fluctuation pattern of standard kitchen heat and hot water supply heat is expressed as a percentage of heat usage by time zone, stored in the storage unit 33 in advance, and the standard By allocating the total daily heat usage of the estimated kitchen heat and hot water heat according to the fluctuation pattern for each time zone, the kitchen heat usage and hot water heat usage for each time zone are obtained. be able to.

  Next, the kitchen for each day of the day determined as described above, the heat usage for each hot water supply, and the heat for each day of heating estimated by the heating heat usage estimation means 53 By adding all the usage amounts by time zone, the daily heat usage amount by time zone can be easily obtained. In addition, at this time, by converting the amount of heat used for each heating period of the day when all heat is supplied by gas equipment using gas, supply for all heat use related to kitchen, hot water supply, and heating can be achieved. It can be covered by city gas.

  When the system 1 of the present invention is implemented on a computer, the arithmetic processing performed by each means is realized by software processing composed of a plurality of program steps.

  Hereinafter, another configuration example in the present embodiment will be described.

  (1) In the above description, the second usage pattern estimation unit 46 did not confirm whether or not the energy usage by time period in the summer and winter periods is different from the past actual value. However, when there are actual values in summer or winter, the same correction as in the intermediate period may be performed in each one period of summer or winter.

  In this case, the second usage pattern estimator 46 inputs the power usage by time period for all the time zones calculated by the usage pattern estimator 42 to the period power usage calculator 43 and accumulates the power. As in the case of the intermediate period, the usage amount comparison means 44 stores the past actual value of the accumulated power consumption and the period power consumption calculation means for the period other than the intermediate period of the energy consumer stored in the storage unit 33 in advance. The error is calculated by comparing the accumulated power usage amount calculated by 43.

  The calculated error is displayed on the display unit 32 side by side with, for example, the past actual value and estimated value of the accumulated power usage, or the past actual value, estimated value, and error rate (= error / (Actual value × 100%) are displayed side by side. Here, together with the error display, the estimation result of the fluctuation pattern of the daily power consumption as shown in FIG. 3 is displayed in a graph on the same screen.

Therefore, the operator selects one of the first characteristic time zones t _{P1 to} t _P3 and the second characteristic time zones t _{B1 to} t _B3 from the correction input screen displayed on the display unit 32 by the power estimation correcting means 45. Specify one time zone and enter the amount of power usage correction for that time zone. The operator inputs an appropriate correction amount estimated from the displayed error. As the input of the correction amount, an increase / decrease rate (correction amount / estimated power usage by time zone × 100%) with respect to the estimated power usage by time zone may be input.

The power estimation correcting means 45 determines, according to the ratio of the standard deviation in summer or winter, from the correction amount in the specified time zone, according to the time zone of the other unspecified first feature time zone and second feature time zone. Calculate the amount of power usage correction. The ratio of the winter correction amount based on the standard deviation in the first characteristic time zones t _{P1 to} t _P3 and the second characteristic time zones t _{B1 to} t _B3 used in the present embodiment is uniquely determined by the administrative district information G.

  The power estimation correcting means 45 accepts the correction of the power usage by time zone in one time zone, calculates the correction amount of the power usage by time zone in the remaining five time zones, and the power by time zone after these corrections The usage amount is input to the hourly usage amount estimation means 41, and the estimation process is executed again. The usage pattern estimation means 42, the period power usage calculation means 43, the accumulated power usage comparison 44, and the power estimation correction means 45 calculate an intermediate estimation result again in the same manner, and the intermediate estimation result is the estimation. The error is displayed on the display unit 32. As in the case of the interim period, the operator repeats this until the estimation error falls within a certain minute range (for example, within ± 5%).

In this way, when the power usage amount for each time period in the winter period or summer period is corrected to match the actual value, the previously calculated temperature correction amounts A _{P1 to} A _P3 and A _{B1 to} A _B3 are calculated. or _{B P1 ~B P3,} B _{B1 ~B} the _B3 was modified based on the time zone power usage corrected, another period was not coordinated with the actual value, the temperature correction amount a of the modified using _P1 ~A _{P3, a} B1 ~A _B3 or _{B P1 ~B P3, B B1 ~B} B3, performing recalculation.

(2) In the above description, the second usage pattern estimator 46 is started after the estimation error falls within the fixed minute range in the intermediate period (specific period), and the error falls within the minute range. Based on the power consumptions E _{P1 to} E _P3 and E _{B1 to} E _B3 for each time period estimated by the time period use amount estimation means 41, the same estimation process as that for the intermediate period is performed for periods other than the intermediate period Was the form to execute. Instead, the second usage pattern estimation unit 46 firstly uses the hourly usage amount estimation unit 41 for each hourly power usage amount E _{P1 to} E _P3 and E _B1 to E _B1 to E _B1 . After estimating E _B3 , it is started before each means 42 to 45 related to the subsequent processing is started, and for other periods, temperature correction is performed based on the difference in average temperature, and the power consumption amount E _{P1 to} E for each time zone is corrected. _P3 and E _{B1 to} E _B3 may be estimated, and a series of processes similar to those in the intermediate period may be executed for an arbitrary period. In this case, for the intermediate period, the estimation process of the power consumption can be performed for an arbitrary period without performing the correction process of the estimation result.

(3) In the above description, the operator, from the correction input screen displayed on the display unit 32 by the power estimation correction means 45, the first characteristic time zone t _{P1 to} t _P3 and the second characteristic time zone t _{B1 to} t _B Specify any one of _B3 and enter the correction amount of the power usage amount for each time zone in that time zone, and the correction amount is automatically set for the other first feature time zone and second feature time zone However, the operator does not input the correction amount, and the hourly usage amount estimation unit 41 is provided with a power usage amount correction unit (not shown) for each time zone. The power consumption correction means uses the ratio of the correction amount based on the standard deviation in the first feature time zone t _{P1 to} t _P3 and the second feature time zone t _{B1 to} t _B3 so that the estimation error is eliminated. The first feature time zone and the second feature time zone of It may alternatively be configured to calculate the correction amount of the dose. The correction algorithm may basically be the same as the processing procedure performed by the operator in the above embodiment.

(4) In the above description, the first characteristic time zones t _{P1 to} t _P3 and the second characteristic time zones t _{B1 to} t _B3 are fixedly set in advance based on a standard home life pattern. Alternatively, the standard setting may be changed at any time by an operator's input operation. In the latter case, each characteristic time zone may be changed when input data is input or correction input is input. For example, the wake-up time used in the standard setting functions effectively when there is a large discrepancy with the wake-up time of the household subject to power supply evaluation.

When the lifestyle changes depending on the area, the first characteristic time zone t _{P1 to} t _P3 and the second characteristic time zone t _{B1 to} t _B3 are changed according to the value of the third data (administrative district information G). Also good.

(5) In the above description, as input data common to the first and second data, the family structure of the visited home (the number of families N (persons)) and the presence or absence of day-time homes (with homes: X = 1, None: X = 0) and floor area (S (m ³ )), but the input data is not limited to that of the above embodiment. For example, not only the number of people but also attribute information such as occupation, adult / child distinction, etc. may be added to the family structure. Also, the number of people may be used instead of the presence or absence of a daytime home. Furthermore, power consumption equipment that affects bottom demand in the second characteristic time period t _{B1 in} the early morning, such as the number of refrigerators, toilet seats with heaters, water tanks, and the number of lights lit during sleep, etc. The type and number of devices that are not stopped may be added to the input data as necessary.

(6) In the above description, the first characteristic time zones t _{P1 to} t _P3 are assumed assuming the fluctuation pattern of the daily power consumption as illustrated in FIG. Although the second characteristic time zones t _{B1 to} t _B3 are set, the number of the first characteristic time zones t _{P1 to} t _P3 and the second characteristic time zones t _{B1 to} t _B3 to be used is increased / decreased depending on the presence or absence of a person in the daytime. Thus, the interpolating process of the power usage amount by time zone in the intermediate time zone may be made different.

(7) In the above description, the accumulated power consumption for a specific period is calculated with the fluctuation pattern of the daily power consumption being common within the period. For example, the daily power consumption is divided into weekdays and weekends. You may make it estimate the fluctuation pattern of quantity. In other words, if the input data differs between the weekday and the weekend, the power usage for each time zone is estimated for each time zone t _{P1 to} t _P3 and t _{B1 to} t _B3 for each week day and weekend. A fluctuation pattern of the usage amount may be derived. In addition, separate regression formulas may be provided for weekdays and weekends.

  (8) In the above description, the hot water supply heat demand was estimated by subtracting the kitchen heat use amount from the measured value of the total heat use amount in the interim period, but the hot water supply heat demand amount is also estimated by providing its own regression equation. However, it may be estimated by correcting the measured value. At this time, when considering the geographical characteristics of hot water supply heat demand, the coefficient of the regression equation may be determined according to the administrative district information G.

  (9) The regression equation and the interpolation method used in the above description are examples, and are not limited to these. Moreover, the time slot | zone which comprises 1 day is not limited to 1 hour, For example, you may be 30 minutes or 2 hours. Further, the period constituting one year is not limited to one month, and may be, for example, two months.

  (10) In the above description, city gas is assumed as an energy medium capable of generating electric power and heat. However, the energy medium may be other than city gas. For example, it may be solar energy. In this case, heat may be generated directly by using solar heat or indirectly by converting electric power generated by solar power generation into heat energy. When the energy medium is solar energy, the energy usage fee is set to 0 and a predetermined evaluation value for use cost evaluation may be calculated.

  (11) In the above description, both the power usage estimation means 2 and the heat usage estimation means 3 are configured to use the third data (administrative district information G) for the calculation process. Only one of them may use the third data.

  (12) In the above description, when estimating the heat usage for every time period of the day, the daily fluctuation pattern of the standard kitchen heat and hot water supply heat from the past performance data etc. Separately expressed as a percentage of heat usage and stored in the storage unit 33, the total heat usage of the kitchen heat and hot water heat estimated along the standard fluctuation pattern is assigned to each time zone. However, the configuration may be such that the kitchen heat and hot water supply heat usage in the peak time zone is calculated by a regression equation including the temperature or water temperature information.

  At this time, the peak time zone may be matched with the peak time zone of the power usage estimated by the usage estimator 41 for each time zone. In this case, there are three types of peak time zones: morning, noon, and night.

  After calculating the amount of heat used in the peak time zone by a regression equation, the amount of heat used in other time zones estimated by each estimating means is based on the ratio determined for each time zone as described above. Similar to the method performed by the usage pattern estimation means 42, the heat usage during another time zone may be estimated by performing an interpolation process.

  (13) In the above description, the amount of heat used for hot water supply is calculated based on the value of the kitchen heat usage estimated by the kitchen heat usage estimation means. A value calculated every month based on a regression equation determined based on the second data and the third data may be used as the hot water supply heat consumption.

<Second Embodiment>
Hereinafter, a second embodiment of the system of the present invention (hereinafter referred to as “this embodiment” as appropriate) will be described with reference to FIGS.

  Compared with the first embodiment, the present embodiment is a configuration in which the power usage amount estimation method in the power usage amount estimation unit 2 is different, and other components are the same as those in the first embodiment. The description is omitted.

  In the present embodiment, a one-year power usage fee is used as the first data instead of the actual value of the accumulated power usage in the specific period used in the first embodiment. The data related to the power usage fee may be a value reported by the target energy consumer.

  FIG. 9 is a block diagram showing a schematic configuration of the power usage amount estimation means in the present embodiment. The power usage amount estimation means 2a shown in FIG. 9 has a configuration that does not include the second usage amount pattern estimation means 46 as compared with the power usage amount estimation means 2 in the first embodiment shown in FIG. Hereinafter, the power usage amount estimation method according to the present embodiment will be described with reference to FIG. In addition, since the power usage amount estimation method in the present embodiment has the same part as the power usage amount estimation method described in the first embodiment, the description of the part is simplified, and only different parts are described in detail. An explanation shall be given.

  As described above, in this embodiment, in addition to the family composition information N, daytime presence / absence information X, floor area information S, and administrative district information G, information about the annual power rate is used. In the following description, information about the annual power rate will be described with the symbol “F” added as appropriate.

  Each information S, N, X, F, and G input from the input unit 31 is first stored in the storage unit 33. At this time, information related to the power unit price is stored in the storage unit 33, and the annual power consumption of the target energy consumer can be calculated from the input annual power rate F and the information related to the power unit price. It is assumed that A charge conversion means (not shown) is provided in the power consumption estimation means 2a, and after the annual power consumption is calculated using the power unit price and the annual power charge F by the means, this value is stored. It is good also as a structure sent to the part 33. FIG. In the following description, it is assumed that annual power usage information corresponding to the annual power charge F is stored in the storage unit 33.

  Similarly to the first embodiment, the hourly usage amount estimation unit 41 determines each peak and bottom demand based on a regression equation that changes according to the input information S, N, X, and G. At this time, in this embodiment, it is assumed that the regression equation for 12 months is registered in advance, and the peak demand amount and the bottom demand amount for 12 months are determined by the input information. Furthermore, different regression equations may be prepared for weekdays and holidays (in this case, 24 types of regression equations are generated).

  Even if the regression equation for 12 months is not registered, it is sufficient if the peak demand amount and the bottom demand amount for 12 months are determined by the input information. A different regression equation may be generated for each month based on the average temperature.

  The monthly peak demand value and the bottom demand value generated by the hourly usage amount estimation means 41 are sent to the usage pattern estimation means 42. The usage pattern estimator 42 performs interpolation processing in the same manner as in the first embodiment, and estimates the power usage by time period for 24 hours. As described above, since the hourly usage amount estimation means 41 is configured to determine the peak demand amount and the bottom demand amount for each month, the peak and bottom demand values for each month are given. Thus, the usage pattern estimation unit 42 estimates the power usage by time period for 24 hours every month. If the peak-and-bottom demand amount is determined for each weekday holiday by the hourly usage estimation means 41, the usage pattern estimation means 42 for each weekday holiday further determines the power usage amount by hour for the 24 hours. Estimation is performed.

  The power usage by time period estimated by the usage pattern estimation unit 42 is given to the period power usage calculation unit 43. The period power usage calculation means 43 calculates the total value of the estimated power usage for one year of the energy consumer. As described above, the usage pattern estimation unit 42 estimates the power usage amount for each time zone corresponding to 24 hours for each month. Therefore, by accumulating these values, the estimated power consumption for one year is estimated. The total amount of usage can be calculated (hereinafter referred to as “estimated annual power usage”). In addition, when it is given for each weekday holiday, the total value is calculated in consideration of that separately.

  The estimated annual power usage calculated by the period power usage calculation means 43 is given to the accumulated power usage comparison means 44. The accumulated power usage comparison unit 44 reads the annual power usage of the energy consumer from the storage unit 33, compares it with the estimated annual power usage given by the period power usage calculation unit 43, and calculates an error. Then, the calculated error is given to the power estimation correcting means 45.

  The power estimation correction means 45 apportions the annual error value given from the accumulated power usage comparison means 44 every month by a predetermined method. This apportioning method will be described below.

  In the present embodiment, the power load is divided into a base load that does not vary throughout the year, a cooling load in which demand appears in the summer, and a heating load in which demand appears in the winter. A plurality of power demand samples are acquired in advance under various conditions, and these are decomposed into a base load, a cooling load, and a heating load, and an average value and a standard deviation are calculated in each. It is assumed that the average value and the standard deviation value are stored in the storage unit 33 for each condition and for each type of load. Note that the regression equation used in the hourly usage amount estimation means 41 may also be generated from a plurality of power demand samples acquired in advance.

  The power estimation correcting means 45 apportions the error given from the accumulated power usage comparing means 44 in accordance with the standard deviation ratio for each load given from the storage unit 33. For example, when the error value is 2 MHh, the standard deviation of the base load is ± 3 MWh, the standard deviation of the heating load is ± 4 MWh, and the standard deviation of the cooling load is ± 1 MWh, the error values are converted into the base load and the heating load. The distribution is performed at a ratio of 3: 4: 1 for each cooling load (in this case, 0.75 MWh is allocated for the base load, 1 MWh for the heating load, and 0.25 MWh for the cooling load). Based on the distributed error value, correction for each load is performed. This correction will be described with reference to FIG.

  FIG. 10 is a graph of the monthly power usage estimated by the hourly usage usage estimation means 41, with the total value calculated for each month. Here, the cooling season is defined as a month with an average temperature of 24 ° C. or higher, and the heating season is defined as a month with an average temperature of 14 ° C. or lower. The temperature used for the definition of the cooling season and the heating season is defined as this temperature. It is not limited. In addition, it is assumed that the monthly average temperature information is stored in the storage unit 33. Hereinafter, as a result of classification based on the above definition, the heating period is from December to March, and the cooling period is from July to September.

  The monthly value indicating the minimum power usage amount among the monthly power usage amounts is defined as the base load. In the example of FIG. 10, the power usage amount in May corresponds to the base load value. At this time, a difference from the base load value in the heating period is defined as a heating load, and a difference from the base load value in the cooling period is defined as a cooling load. According to the example in FIG. 10, the difference between the power usage in May to December and December corresponds to the heating load, and the power usage in May from June to August is equivalent to the heating load. The deviation from the power consumption corresponds to the cooling load.

  Under such a state, a case where correction of 0.75 MWh as a base load, correction of 1 MWh for a heating load, and correction of 0.25 MWh for a cooling load will be described.

  FIG. 11 is a graph showing the electric power consumption for each month after correction. Since the base load is assumed to be a power usage amount that does not change throughout the year, the correction amount of 0.75 MWh is divided into 12 months, and correction of 0.063 MWh is performed for each month (white area in FIG. 11). In addition, since the heating load is a load that is recognized during the four months from January to March and December, dividing the correction amount of 1 MWh into four months makes 0.25 MWh for each month from January to March and December. Is corrected (the black portion in FIG. 11). Furthermore, since the cooling load is a load that is recognized during the three months from June to August, the correction amount of 0.25 MWh is divided into three months, thereby correcting 0.083 MWh for each month from June to August. (Vertical stripe portion in FIG. 11).

  After correcting each month in this way, based on the correction amount, the hourly power consumption estimated by the hourly usage amount estimation unit 41 is corrected for each month. As this correction method, for example, the ratio of the cumulative power consumption before and after the correction is obtained every month, and the estimated power usage by time zone is uniformly the same according to the ratio for each hour. It is possible to make corrections, and the monthly correction amount is converted to one day, and the amount of power used by time period estimated by equally dividing the converted daily correction amount in 24 hours. May be corrected by increasing / decreasing for each time zone, and if an index (standard deviation) indicating the degree of variation for each time zone is given in advance, the correction amount May be further divided according to the ratio of the standard deviation, and the correction may be performed by increasing or decreasing the apportioned correction amount for each time zone with respect to the estimated power usage amount by time zone. Furthermore, the value of the magnitude of the variation may be changed according to the administrative district information G.

  The power estimation correcting means 45 performs the correction as described above, so that the correction is made according to the past annual power charge of the target energy consumer, and the state 24 in which the past annual power charge is reflected every month. It is possible to calculate the power usage amount by time zone related to time. In particular, since the error is apportioned based on the standard deviation of the base load, heating load, and cooling load, more realistic error correction can be performed. In particular, the standard deviations of the base load, the heating load, and the cooling load are values calculated using past sample data of energy consumers under the same conditions (information S, N, X, and G are the same). Therefore, a more realistic correction can be performed.

<Third Embodiment>
Hereinafter, a third embodiment of the system of the present invention (hereinafter referred to as “this embodiment” as appropriate) will be described with reference to FIG.

  In this embodiment, a monthly power usage fee is used as the first data. That is, in the second embodiment, it can be used when the annual power usage fee is known, but in this embodiment, it can be used when the monthly power usage fee is further known. It is a form. The data related to the power usage fee may be a value reported by the target energy consumer.

  FIG. 12 is a block diagram showing a schematic configuration of the power usage amount estimation means in the present embodiment. The power usage amount estimation means 2b shown in FIG. 12 has the same configuration as the power usage amount estimation means 2a shown in FIG. 9, and the type of information given to the cumulative power usage amount comparison means 44 is different.

  Also in this embodiment, each information S, N, X, and G is input from the input unit 31 and stored in the storage unit 33 as in the second embodiment. In addition, a monthly power charge F is input, and a power conversion amount (not shown) calculates a power usage amount for each month and stores it in the storage unit 33.

  Similarly to the second embodiment, the hourly usage amount estimation means 41 determines the peak and bottom demand amounts for each month based on the regression equation that changes according to the input information S, N, X, and G, and uses the usage amount. This is given to the pattern estimation means 42. The usage pattern estimator 42 performs the above-described interpolation process, estimates the power usage by time period for 24 hours for each month, and provides the estimation result to the electrical period power usage calculator 43.

  The period power consumption calculating means 43 calculates a total value of estimated power consumption for one month of the energy consumer (hereinafter referred to as “estimated monthly power consumption”). At this time, if the peak-and-bottom demand amount is determined for each weekday holiday in the usage estimation means 41 for each time zone, the total value is calculated for each month in consideration of the number of weekdays and the number of holidays. .

  The estimated monthly power usage for each month calculated by the period power usage calculation means 43 is given to the cumulative power usage comparison means 44. The accumulated power usage comparison unit 44 reads the monthly power usage of the energy consumer from the storage unit 33, compares it with the estimated monthly power usage given by the period power usage calculation unit 43, and calculates an error. To do. Then, the calculated error is given to the power estimation correcting means 45.

  Also in the present embodiment, as in the second embodiment, the power load is divided into a base load that does not vary throughout the year, a cooling load in which demand appears in summer, and a heating load in which demand appears in winter. A plurality of power demand samples are obtained in advance under various conditions, and these are decomposed into a base load, a cooling load, and a heating load, and an average value and a standard deviation are calculated every month.

  The power estimation correction means 45 apportions the error given from the accumulated power usage comparison means 44 every month according to the standard deviation ratio for each load given from the storage unit 33. In other words, the correction is made for each month by apportioning the error calculated for each month based on the standard deviation value for each month.

  When the correction is performed for each month, the power usage amount for each time zone estimated by the usage amount estimation unit for each time zone 41 is corrected for each month based on the correction amount. This correction method may be the same method as described above in the second embodiment.

  By being configured in this way, it is possible to correct the estimated power amount every month, and thus it is possible to perform more strict correction as compared with the second embodiment.

<Fourth Embodiment>
Hereinafter, a fourth embodiment of the system of the present invention (hereinafter referred to as “this embodiment” as appropriate) will be described with reference to FIG.

  The present embodiment is different from the first to third embodiments in the heat usage estimation method in the heat usage estimation means 3, and the other components are the first to third each. Since it is the same as that of embodiment, the description is abbreviate | omitted.

  FIG. 13 is a block diagram showing a schematic configuration of the heat usage estimation means in the present embodiment. Compared with the heat usage estimation means 3 shown in FIG. 8, the heat usage estimation means 3 a shown in FIG. 13 includes a period heat usage calculation means 54, a cumulative heat usage comparison means 55, and a heat estimation correction means 56. It is the composition further provided.

Moreover, in this embodiment, it replaces with the actual value of the cumulative heat usage amount in the specific period used in the 1st-3rd embodiment as 2nd data, and uses the gas usage fee for every month. The data on the gas usage fee may be a value reported by the target energy consumer. In the following, information on the gas usage fee for each month will be described with the symbol “F _g ” added as appropriate.

  The kitchen heat usage estimation means 51, the hot water supply heat usage estimation means 52, and the heating heat usage estimation means 53 are determined based on the second data input from the input unit 31, as in the above-described embodiments. Based on the regression equation and the fluctuation pattern registered in advance, the estimated heat usage by time zone is calculated. At this time, the regression equation includes the temperature information, and the average temperature for each month is read from the storage unit 33 so that the estimated heat usage for each time zone is calculated for each month. preferable.

The storage unit 33 stores information related to the gas unit price, and the monthly gas of the target energy consumer is determined based on the input monthly gas usage fee _Fg and the information related to the gas unit price. It is assumed that the usage amount can be calculated. Further, the heat amount estimating means within 3a, rates conversion means (not shown) are provided, the gas used for each month by using the gas use fee F _g per gas unit price and each month by the means After the amount is calculated, this value may be sent to the storage unit 33.

  The heat usage estimated by each means is given to the period heat usage calculation means 54, and the period heat usage calculation means 54 calculates the total usage for each month for each application.

  When the total heat usage for each month is calculated for each application in the period heat usage calculation means 54, the gas usage for each month is read from the storage unit 33, and based on the value of the gas usage. Then, the amount of gas used is decomposed for each application, and an error is calculated for each application.

  FIG. 14 is a diagram for explaining a method of decomposing each usage from the monthly gas usage. FIG. 14A is a graph of the amount of gas used every month as it is.

  First, the amount of heat for use in the kitchen for each month is determined from the amount of use of the kitchen heat estimated by the kitchen heat use amount estimation means 51 (FIG. 14B). The amount of kitchen heat used is represented by diagonal stripes in FIG.

  Next, assuming that the month in which the gas is used for heating is limited to the winter season and, for example, December to March corresponds to the winter season according to the above definition, the other April to 11 For the month, since all heat uses other than kitchen use are for hot water supply, hot water supply in April to November is calculated by subtracting the determined kitchen heat use from the monthly gas use. The amount of heat used is fixed. Here, the determined amount of hot water supply heat used is represented by vertical stripes in FIG.

  At this time, from April to the lowest heat usage month (July according to the example of FIG. 14) and each gas usage amount from the lowest heat usage month to November are approximately connected by a straight line, and the straight line is also connected. They extend outwards in January and December directions (corresponding to the dotted lines in FIG. 14B). And it decomposes | disassembles into a kitchen heat use and a heating heat use by making into a boundary the position which the graph which the gas usage-amount of December to March shows and each straight line cross | intersect (FIG.14 (c)). The heating heat usage determined at this time is shown in black in FIG.

In this way, when the gas usage amount for each application is decomposed for each month, the monthly usage amount for each application and the estimated monthly usage amount for each application given by the period heat usage calculation means 54 To calculate the error. In addition, about the kitchen heat usage, since the value estimated by the kitchen heat usage estimation means 51 at the time of use decomposition | disassembly is utilized, only the error of hot water supply heat and heating heat will appear here.

  The error for each application calculated in this way is given to the heat estimation correcting means 56, and the heat estimation correcting means 56 reads out the standard deviation calculated for each application in advance from the storage unit 33, and Error correction is performed by allocating the error based on the deviation. This standard deviation value is obtained by obtaining a sample of a plurality of gas usages under various conditions in advance and decomposing the gas usage into each application by the same application decomposition method as described above. A standard deviation derived based on the amount used can be used. Therefore, the standard deviation may be different depending on the input second data and third data.

  In addition, the above method of estimating the hot water use amount and the heating heat use amount in winter by linearly approximating the gas use amount of each month is merely an example, and is not limited to this method. For example, a relational expression between water temperature or temperature and heat usage is derived from each gas usage, and using the relational expression, the hot water supply usage and heating heat for the period are determined from the water temperature or temperature in winter (December to March). It is good also as what estimates the usage-amount. That is, the heat demand other than the winter season (April to November) is composed of hot water supply demand and kitchen demand, and the amount of use of kitchen heat is considered to be uniform over the year. By deriving a relational expression between the usage amount and the water temperature or temperature, a relational expression between the hot water supply heat usage and the water temperature or temperature is derived. By using this derived relational expression, the hot water supply heat consumption amount in the winter season (December to March) is calculated from the value of the water temperature or temperature for each month. By subtracting the calculated hot water supply heat usage and uniform kitchen heat usage from the total heat usage for each month, the heating heat usage for each month in the winter can be calculated.

  Note that the kitchen heat usage is substantially constant throughout the year, and the kitchen heat usage estimation means 51 is configured to calculate the total monthly usage of the representative month used for kitchen usage. At the time of use decomposition, the calculation may be performed on the assumption that the kitchen heat usage amount is equal to the total monthly usage amount related to the kitchen usage in this representative month.

  Also in the present embodiment, a correction value may be configured to be manually input from the input unit 31 to the heat estimation correction means 56, similarly to the power usage estimation means 2, 2a, and 2b. .

<Fifth Embodiment>
Hereinafter, a third embodiment of the system of the present invention (hereinafter, referred to as “this embodiment” as appropriate) will be described with reference to the drawings.

  FIG. 15 is a schematic configuration diagram showing the configuration of the system of the present invention in this embodiment. The system of the present invention uses the system of the present invention 1 of the first embodiment as a server to instruct the calculation of the power and gas usage charges from each client terminal via the telecommunication line via the first and second energy supply modes. The system 1 of the present invention is constituted by a server client system that returns the calculation result to the client terminal.

  A network system 60 shown in FIG. 15 includes a server 61 equipped with the system 1 of the present invention in the first embodiment, and client terminals 62a, 62b,. It consists of. In FIG. 15, the client terminals 62 a, 62 b,... Are illustrated as if they can be connected to each other via an electric communication line, but the client terminals are not necessarily configured to be connectable. There is no need, and it is only necessary that each client terminal 62a, 62b,...

  For example, assume that a mobile notebook computer is used as the client terminal 62a, and an operator carries the terminal and visits an energy consumer's home. The computer is configured to be connectable to the server 61 via a wireless line.

  The operator operates the client terminal 62a to connect to the server 61 via a wireless line. At this time, the server 61 may perform connection authentication for the purpose of eliminating connections from outsiders. At this time, for example, in addition to the employee number of the operator, a configuration in which the contract number of the energy consumer's house is input may be configured such that the third data is sent to the server 61 simultaneously with the authentication.

  When the server 61 accepts the connection authentication from the client terminal 62a, the server 61 performs the authentication determination. If the server 61 is an operator who can permit the connection, the server 61 gives information to that effect to the client terminal 62a and starts the connection. At this time, an input screen for inputting the first data and the second data is displayed on the client terminal 62a. If the third data is not provided to the server 61 at the same time as the authentication, the third data is also requested to be input.

  The operator inputs the first and second data (and the third data) according to the display screen of the client terminal 62a. The server 61 performs the calculation by the method described in the first embodiment based on the first to third data input from the client terminal 62a.

At this time, if the error calculated by the accumulated power usage comparing means 44 is an error value having a magnitude exceeding a predetermined range, the power estimation correcting means 45 causes the client terminal 61a to display a correction input screen. The operator operates the client terminal 62a to select one or all of the first characteristic time zones t _{P1 to} t _P3 and the second characteristic time zones t _{B1 to} t _B3 , For each selected characteristic time zone, an appropriate correction amount estimated from the displayed error is input, and the input value is given to the server 61. Since the calculation method has been described in the first embodiment, a description thereof will be omitted.

  By configuring in this way, the calculation itself is performed in the server 61. Therefore, for example, even when reviewing the regression equation variables used in the calculation, it is only necessary to change the numerical value in the server 61. Since it is not necessary to change the arithmetic expression in each, the versatility as a system is high. That is, each client terminal 62a, 62b,... Suffices if at least a general-purpose browser for accessing the server 61 is installed, and the arithmetic processing related to the energy supply evaluation described in the first embodiment is performed. There is no need to implement software to do it.

The conceptual diagram for demonstrating the energy supply form in the energy supply evaluation system which concerns on this invention 1 is a schematic configuration diagram showing a configuration of a first embodiment of an energy supply evaluation system according to the present invention. The graph which shows an example of the estimation result by the electric power consumption estimation means and the heat usage estimation means of the energy supply evaluation system which concerns on this invention Processing flow chart of operation plan determination by cogeneration pattern calculation means of energy supply evaluation system according to the present invention The schematic block diagram which shows the structure of the electric power consumption estimation means of the energy supply evaluation system which concerns on this invention A graph showing the fluctuation pattern of daily power consumption in the home The graph for demonstrating an example of the interpolation process by the usage-amount pattern estimation means of the energy supply evaluation system which concerns on this invention The schematic block diagram which shows the structure of the heat usage estimation means of the energy supply evaluation system which concerns on this invention The block diagram which shows schematic structure of the electric power consumption estimation means in 2nd Embodiment of the energy supply evaluation system which concerns on this invention. A graph showing the estimated monthly power consumption A graph that displays the power usage after correcting the estimated monthly power usage The block diagram which shows schematic structure of the electric power usage-amount estimation means in 3rd Embodiment of the energy supply evaluation system which concerns on this invention. The block diagram which shows schematic structure of the heat usage estimation means in 4th Embodiment of the energy supply evaluation system which concerns on this invention. Diagram for explaining how to disassemble each usage based on monthly gas consumption The block diagram which shows schematic structure in 5th Embodiment of the energy supply evaluation system which concerns on this invention. Explanatory drawing which shows an example of the action schedule table used with the estimation method of the conventional energy demand Explanatory drawing which shows an example of the registration data of the energy consumption apparatus used with the estimation method of the conventional energy demand Graph showing regional characteristics of heating use Graph showing regional characteristics of cooling use

Explanation of symbols

1: Invention system 2, 2a, 2b: Electric power consumption estimation means 3, 3a, 3b: Heat usage estimation means 4: First evaluation value calculation means 5: Second evaluation value calculation means 6: Combined heat and power pattern calculation means 10: 1st energy supply form 11: System power supply 12: City gas network 13: House 14: Electric power load 15: Thermal load 16: Other gas equipment 17: Gas water heater 20: 2nd energy supply form 21: Cogeneration system 22 : Boiler 23: Hot water storage tank 24: Heat exchanger 25: Electric heater 31: Input unit 32: Display unit 33: Storage unit 41: Usage amount estimation unit for each time zone 42: Usage pattern estimation unit 43: Periodic power consumption calculation Means 44: Accumulated power usage comparison means 45: Power estimation correction means 46: Second usage pattern estimation means 51: Kitchen heat usage estimation means 52: Hot water supply heat usage estimation means 53: Heating heat usage estimation means 54: Periodic heat usage calculation means 55: Thermal cumulative usage comparison means 56: Heat estimation correction means 60: Network system 61: Servers 62a, 62b,...: Client terminal 63: Electric communication line

Claims (17)

When both an energy medium capable of generating electric power and heat and electric power are supplied from the outside to an energy consumer, the energy medium is either electric power or heat as an energy supply form for electric power demand and heat demand. An energy supply evaluation system that evaluates the respective usage costs of a first energy supply configuration that only generates power and a second energy supply configuration in which the energy medium generates both power and heat,
A power usage amount estimating means for estimating a power usage amount for each time period of one day by performing a calculation process based on predetermined first data;
Heat usage estimation means for estimating the heat usage by time of day by performing a calculation process based on predetermined second data;
Provided from the outside to cover the daily power usage estimated by the power usage estimation means and the daily heat usage estimated by the heat usage estimation means in the first energy supply mode. A first evaluation value calculating means for calculating a necessary daily supply amount of the energy medium and the energy medium, and calculating a predetermined evaluation value for the use cost evaluation;
Supplied externally to cover the daily power usage estimated by the power usage estimation means and the daily heat usage estimated by the heat usage estimation means in the second energy supply mode. And a second evaluation value calculating means for calculating a predetermined evaluation value for the usage cost evaluation, and calculating each required supply amount of the daily power and the energy medium.
An arithmetic processing method for at least one of the power usage amount estimation means and the heat usage amount estimation means to estimate the power usage amount or the heat usage amount according to predetermined third data input. An energy supply evaluation system characterized by changing.   The energy evaluation system according to claim 1, wherein the third data is information for identifying a difference in lifestyle of the energy consumer or information for identifying a difference in geographical information. . The power consumption estimation means is
While the configuration is to change the arithmetic processing method according to the third data,
Among a plurality of time zones obtained by dividing one day, a plurality of first characteristic time zones having a large amount of power usage determined based on a daily life pattern of the energy consumer and a plurality of second times having a small amount of power usage. A usage estimation unit for each time zone for estimating each power usage in the characteristic time zone based on the first data per arbitrary day;
Usage pattern estimation means for estimating a variation pattern of power usage in all time zones of the given day based on a plurality of time zone power usage estimated by the time zone usage estimation means;
A period power usage amount calculating means for calculating a first power accumulated usage amount for a predetermined period based on the variation pattern estimated by the usage amount pattern estimating means;
The first power accumulated usage amount is compared with the past usage record or the second power cumulative use amount for the predetermined period calculated based on the past use record. Power accumulated usage comparison means for calculating
A correction input for correcting all or part of the estimated power usage by time period, or power estimation correction means for correcting the estimated amount of the fluctuation pattern of the power usage based on the error. The energy evaluation system according to claim 1, wherein:   The energy evaluation system according to claim 3, wherein the second accumulated power usage amount is calculated from a power usage fee related to the predetermined period.   The power estimation correcting means uses the error calculated by the accumulated power usage comparing means for the time-specific power usage in the first or second characteristic time zone according to the magnitude of the variation in power usage. The energy evaluation system according to claim 3, wherein the estimated amount of the fluctuation pattern of the power consumption is corrected based on the error that is prorated.   The said time feature usage amount estimation means changes the said 1st feature time zone and the said 2nd feature time zone according to the said 3rd data, The any one of Claims 3-5 characterized by the above-mentioned. The energy evaluation system described in 1. The time-based usage estimation means is
Deriving each power usage amount in each of the plurality of first feature time zones and the plurality of second feature time zones separately based on a first regression equation in which some or all of the elements constituting the first data are variables. Is a configuration to
The energy evaluation system according to claim 3, wherein the first regression equation is changed in accordance with the third data. The predetermined period is a specific period to which the arbitrary day belongs,
The power consumption estimation means is
Based on the estimated power usage by time period of the plurality of first characteristic time zones and the plurality of second characteristic time zones, a variation pattern of power usage in other periods other than the specific period is estimated. 2nd usage pattern estimation means is provided,
The second usage pattern estimator is configured to use the time-specific power of the first characteristic time zone and the second characteristic time zone based on a difference in average temperature between the specific period and another period other than the specific period. A correction value for correcting the usage amount is calculated, and the power usage amount of all the time periods in one day of the other period is calculated based on the plurality of power usage amounts classified by time period based on the correction value. The energy supply evaluation system according to any one of claims 3 to 7, wherein a fluctuation pattern is estimated. The second usage pattern estimation means is
The correction value is derived on the basis of a second regression equation using a part or all of the elements constituting the first data as variables,
The energy evaluation system according to claim 8, wherein the second regression equation is changed according to the third data.   The power estimation correcting means disassembles the second power accumulated usage according to use, and apportions the error calculated by the power accumulated usage comparing means according to the magnitude of variation for each use, 5. The energy evaluation system according to claim 3, wherein the estimated amount of the variation pattern of the power consumption is corrected based on the apportioned error.   The use as an index when the power estimation correcting means decomposes the second power accumulated usage amount includes a base load whose usage amount is substantially constant throughout the year, a cooling load whose usage amount is recognized in summer, and a winter season. The energy evaluation system according to claim 10, comprising: a heating load whose usage amount is permitted. The heat consumption estimation means is
While the configuration is to change the arithmetic processing method according to the third data,
Kitchen heat usage estimation means for calculating the total heat usage related to the kitchen in a day by a third regression equation using a part of the second data as a variable;
The total amount of heat used for hot water supply for one day, the actual usage amount of the energy medium in the intermediate period when no air conditioning equipment is used, the estimation target period, the average temperature or average water temperature data of the intermediate period, and the kitchen heat use Hot water supply heat usage estimation means for calculating based on the total heat usage for the one-day kitchen calculated by the amount estimation means;
Heating heat usage for calculating the total heat usage related to heating for one day based on the difference between the electricity usage by time of the day in the intermediate period estimated by the power usage estimation means and the winter in which heating is used A quantity estimation means,
A third regression equation or calculation used by at least one of the kitchen heat usage estimation means, the hot water supply heat usage estimation means, and the heating heat usage estimation means for calculation according to the third data The energy supply evaluation system according to claim 1, wherein the expression is changed. The heat consumption estimation means is
Kitchen heat usage estimation means for calculating the total heat usage related to the kitchen in a day by a third regression equation using a part of the second data as a variable;
A hot water use amount estimation means for calculating a total heat use amount related to hot water supply for one day by a fourth regression equation using a part of the second data as a variable;
Heating heat usage estimation means for calculating the total heat usage related to heating for one day based on the value of hourly power usage estimated by the power usage estimation means;
At least one of the kitchen heat usage estimation means, the hot water supply heat usage estimation means, and the heating heat usage estimation means uses a third regression equation used for calculation according to the third data, The energy supply evaluation system according to any one of claims 3 to 11, wherein four regression equations or arithmetic expressions are changed.   The said kitchen heat usage estimation means and the said hot water supply heat usage estimation means determine the 3rd feature time zone which is a time zone with much heat usage based on the said 1st feature time zone. Item 14. The energy supply evaluation system according to Item 13. The cumulative amount of kitchen heat usage for a predetermined period is calculated based on the amount estimated by the kitchen heat usage estimation means, and the total amount of hot water supply heat usage for the predetermined period is estimated by the hot water supply heat usage estimation means. A period heat usage calculating means for calculating a cumulative amount of heating heat usage for the predetermined period based on the amount estimated by the heating heat usage estimating means;
Hot water use amount, kitchen heat use amount, heating heat use amount, and period heat use calculation for the predetermined period calculated based on past use results or values determined according to past use results The heat accumulated usage comparison means for comparing each usage value calculated by the means and calculating the error,
The error is apportioned according to the degree of variation for each application of hot water supply, kitchen, and heating, and the total heat usage related to the kitchen for the day based on the apportioned error, the hot water supply for the day The energy evaluation system according to claim 13, further comprising: a heat estimation correcting unit that corrects the total heat usage related to the heating and the total heat usage related to the heating for the day.   An energy supply evaluation program comprising program steps for realizing each means of the energy supply evaluation system according to any one of claims 1 to 15 by software processing on a predetermined computer. The energy supply evaluation system according to any one of claims 1 to 15, comprising each means, and a configuration that can be connected to a client terminal via a telecommunication line,
When the first data, the second data, and the third data are input from the client terminal via the telecommunication line, the power usage amount estimation unit and the heat usage are based on the input data. Estimate the amount of power used by the time of day and the amount of heat used by the time of day by the amount estimation means,
The first evaluation value calculating means calculates an evaluation value for use cost evaluation for covering the estimated power usage and heat usage in the first energy supply mode,
The second evaluation value calculating means calculates an evaluation value for use cost evaluation for covering the estimated power usage and heat usage in the second energy supply mode,
An energy supply evaluation system characterized in that each evaluation value calculated by the first evaluation value calculation means and the second evaluation value calculation means can be transmitted to the client terminal via the telecommunication line. .