JP2003097846A - House energy consumption calculation data creation device, house energy consumption calculation data creation program, and computer-readable recording medium recording the program - Google Patents

Abstract

(57) [Summary] [Problem] To provide a data generation device for calculating energy consumption of a house, which can generate data capable of predicting energy consumption in a state suitable for a house to be predicted. SOLUTION: Input means I for inputting energy consumption-related actual measurement data, which is actual energy consumption-related actual measurement data of a house, and converts the energy consumption-related actual measurement data input to the input means I based on a predetermined classification condition. Classification means C for classifying, and storage means R for recording the energy consumption-related measured data classified by the classification means C for each classification condition are provided.

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to an energy consumption calculation data creation device for a house, an energy consumption calculation data creation program for a house, and a computer-readable recording medium recording the program.

[0002]

2. Description of the Related Art In designing a heat source of a detached house or an apartment house and designing a device in a house, the energy consumption of the house is evaluated, but the energy consumption calculation of the house according to the present invention is performed. The data creation device for use creates data used when predicting the energy consumption of a house. As such a data generating device for calculating energy consumption of a house, an input unit for inputting energy consumption measurement data obtained by actually measuring the energy consumption of each energy consuming device installed in the house, and the energy input by the input unit. It is conceivable to provide a storage means for storing the actual consumption data. Then, when predicting the energy consumption of the house, by reading the energy consumption actual measurement data corresponding to each energy consuming equipment installed in the prediction target house from the storage means, by adding each read data, The energy consumption of a house will be calculated.

[0003]

By the way, the energy consumption in a house is affected by a characteristic condition (hereinafter, sometimes simply referred to as a characteristic condition) representing a characteristic of energy consumption. As the characteristic condition, for example, there is a home-at-home characteristic indicating the in-home situation of the inhabitants, and a home at-home characteristic having a long home-at-home time tends to consume more energy than a home-attribute characteristic short-at-home. In addition, as a characteristic condition, there is an energy consumption characteristic indicating the amount of energy consumption of a house. Therefore, when predicting the energy consumption of a house, since each prediction target house has a characteristic condition unique to each house, the energy consumption is predicted in accordance with the characteristic conditions unique to the prediction target house. There is a need to.

However, conventionally, since the energy consumption amount measurement data obtained by actually measuring the energy consumption amount of each energy consuming device is simply stored in the storage means, the energy consumption amount measurement data stored in the storage means. Does not know what kind of characteristic condition the data is for a house. Therefore, conventionally, when predicting the energy consumption of a house, it is not known what kind of characteristic condition the data that is the basis for predicting the energy consumption is the data of the house. It was not possible to obtain the consumption amount in a state that matched the characteristic conditions of the house for which the energy consumption was predicted, and thus it was not possible to predict the energy consumption in a state that was suitable for the apartment house for which the prediction was made.

The present invention has been made in view of such circumstances, and an object of the present invention is to generate data capable of predicting energy consumption in a state adapted to the house to be predicted, and the energy consumption of the house. An object of the present invention is to provide a calculation data creation device, a house energy consumption calculation data creation program, and a computer-readable recording medium recording the program.

[0006]

Means for Solving the Problems [Invention of Claim 1]
The characteristic configuration of the data creating apparatus for calculating energy consumption of a house according to claim 1 is: input means for inputting energy consumption related measurement data, which is measurement data related to energy consumption of the house, and the input means inputting the input means. There is provided a classifying unit that classifies the energy consumption-related actual measurement data based on a predetermined classification condition, and a storage unit that records the energy consumption-related actual measurement data classified by the classification unit for each of the classification conditions. There is something to do.
According to the data creation device for calculating energy consumption of a house according to claim 1, when the input means inputs the energy consumption related measurement data which is the energy consumption related measurement data of the house, the classification means causes the input means to enter the input means. The input energy consumption-related actual measurement data is classified based on predetermined classification conditions, and the storage means records the energy consumption-related actual measurement data classified by the classification means for each classification condition. The energy consumption-related actual measurement data includes actual energy consumption measurement data of each house, and information about characteristic conditions obtained by a questionnaire survey of the house where the actual measurement data is measured. That is, the classification condition is set in advance corresponding to the characteristic condition. Then, the input means inputs the energy consumption-related actual measurement data, and the classifying means converts the energy consumption-related actual measurement data into the energy consumption-related actual measurement data based on a predetermined classification condition. It classifies according to the characteristic conditions corresponding to the measured house, and the storage means records the energy consumption-related measured data classified by the classifying means for each classifying condition. Therefore, when predicting the energy consumption of a house, the energy consumption related actual measurement data classified by the classification condition corresponding to the characteristic condition of the target house is read from the storage means, and based on the read data. , The energy consumption of the house will be calculated, but by calculating the energy consumption of the house in this way in accordance with the characteristic conditions of the prediction target house,
The energy consumption of a house can be calculated in a state that is suitable for the predicted target house. By the way, when predicting the energy consumption of a detached house, the energy consumption related actual measurement data corresponding to the classification condition corresponding to the characteristic condition of the detached house will be read and the energy consumption will be calculated. When predicting the total energy consumption of an apartment house, calculate the energy consumption of each house of the apartment house by reading the energy consumption-related measurement data corresponding to the characteristic conditions of each house, and integrate the energy consumption of each house By doing so, it is possible to obtain the total energy consumption of the collective housing. For both single-family houses and collective housing, the energy consumption-related measured data corresponding to the characteristic conditions of the housing is used to measure the energy consumption. Energy consumption of the house is calculated because the consumption is calculated, that is, the energy consumption of the house is predicted. It becomes that can be obtained in a state where costs quantity conforming to the prediction target housing. In addition, in order to be able to obtain the energy consumption of a house in a state that is suitable for the target house, the actual consumption data related to energy consumption should be used for the action schedule, the equipment used, the frequency of use, etc. for each inhabitant of the house. When calculating the energy consumption of a house, the energy consumption related measurement data should be read according to the action schedule, equipment used, frequency of use, etc. for each inhabitant of the house. Although it is conceivable, it is quite troublesome to enter conditions such as action schedule, equipment used, frequency of use, etc. for each inhabitant of the house, and especially when predicting the energy consumption of the housing complex, An action schedule for each resident,
Entering conditions such as equipment used and frequency of use becomes quite complicated. On the other hand, the invention of claim 1 classifies the energy consumption-related data according to the characteristic condition of each house, and the characteristic condition of each house is more than various conditions for each inhabitant. For example, the number of input items can be reduced, so that it is possible to make the conditions less troublesome to input, so when predicting the energy consumption of an apartment house, enter the characteristic conditions for all the houses. Even so, it can be made less troublesome than inputting various conditions for each inhabitant, and is easy to use for predicting the energy consumption amount of an apartment house. In short, according to the present claim 1, when predicting the energy consumption of a house, for calculating the energy consumption of the house, it is possible to create data capable of predicting the energy consumption in a state suitable for the target house A data preparation device can now be provided.

[Invention of Claim 2] The characteristic configuration of the energy consumption calculation data creating apparatus for a house according to Claim 2 is such that a plurality of types of energy consumption-related actual measurement data are input to the input means. Configured to classify each of the plurality of types of energy consumption-related actual measurement data input to the input unit based on a predetermined classification condition corresponding to each type, The storage means is configured to record the energy consumption related actual measurement data of each type classified by the classification means for each classification condition. Claim 2
According to the energy consumption calculation data creation device for a house described in (4), a plurality of types of energy consumption-related actual measurement data are input from the input means, and a plurality of types of energy consumption-related actual measurement input to the input means by the classification means. Each of the data is classified based on a classification condition that is predetermined for each type, and the storage unit records the energy consumption-related measurement data of each type classified by the classification unit for each classification condition. It That is, for example, energy consumption-related measurement for each energy use (corresponding to the type of energy) of electricity (other than hot water supply, heating and cooling, may be referred to as general electricity hereinafter), kitchen, hot water supply, heating and cooling Since each of the data is classified and recorded in the storage means based on the classification condition that is predetermined corresponding to each energy use, for each energy use,
It is possible to calculate the energy consumption of a house in a state that matches the characteristic conditions of the predicted house. Therefore, it has become possible to create data that can predict the energy consumption for each energy application in a state suitable for the target house.

[Invention according to claim 3] The characteristic configuration of the data creating device for calculating energy consumption of a house according to claim 3 is that the classifying means includes the plurality of types of energy consumption input to the input means. Among the related measured data, the measured data related to gas consumption for kitchen, the measured data related to energy consumption for hot water supply, and the measured data of power consumption other than for kitchen, hot water supply and air conditioning ,
It is configured to classify for each of the classification conditions including energy consumption characteristics, family characteristics, and day characteristics.
According to the data generation device for calculating energy consumption of a house according to claim 3, actual measurement data related to kitchen gas consumption, actual measurement data related to energy consumption for hot water supply, and other than for kitchen, hot water supply and air conditioning Each of the measured data of the power consumption (hereinafter, also referred to as general power consumption) is classified by the classification means based on the classification condition including the at-home characteristic, the energy consumption characteristic, the family characteristic and the day characteristic. , Is recorded in the storage means. In other words, regarding energy consumption related to kitchen gas consumption, energy consumption for hot water supply, and general electric power consumption, at-home characteristics (indicating the length of time at home, etc.), energy consumption characteristics (indicating the amount of energy consumption), and occupancy It is relatively susceptible to characteristic conditions such as personal characteristics (indicating family composition) and day characteristics (indicating weekdays, holidays, seasons, weather, etc.). Therefore, the actual measurement data related to kitchen gas consumption, the actual measurement data related to energy consumption for hot water supply, and the actual measurement data of general electric power consumption are classified based on classification conditions including at-home characteristics, energy consumption characteristics, family characteristics, and day characteristics. Then, by recording in the storage means, the energy consumption of the kitchen gas consumption, the energy consumption of the hot water supply and the energy consumption of the general electric power can be calculated, respectively, to calculate the at-home characteristics, energy consumption characteristics, and family characteristics of the predicted home. Moreover, it is possible to obtain the condition more realistically according to the characteristic conditions including the day characteristic. Therefore, it has become possible to create data that can predict the energy consumptions of the kitchen gas consumption, the hot water supply energy consumption, and the general electric power consumption in a state that more closely matches the prediction target house.

[Invention of Claim 4] The characteristic construction of the data creating apparatus for calculating energy consumption of a house according to Claim 4 is that the classifying unit is configured to use the plurality of types of energy consumption input to the input unit. It is configured to classify the actual measurement data related to the energy consumption for air conditioning among the related actual measurement data for each of the classification conditions including the building characteristic, the day characteristic, and the regional characteristic. According to the data generating device for calculating energy consumption of a house according to claim 4, the actual measurement data related to the energy consumption for air conditioning is classified by the classification means by each classification condition including the building characteristic, the day characteristic and the regional characteristic. And is recorded in the storage means. That is, regarding energy consumption related to air conditioning, it is relatively susceptible to characteristic conditions such as building conditions, air conditioning conditions (indicating an air conditioning use time zone, set temperature, etc.), daily characteristics, regional characteristics, and the like. Therefore,
Measured data related to energy consumption for air conditioning can be
By classifying based on the classification conditions including the air conditioning conditions, day characteristics, and regional characteristics, and recording in the storage means, the energy consumption related to air conditioning can be calculated as the building conditions, air conditioning conditions, and day characteristics of the forecasted house. Moreover, it is possible to obtain the condition in a more realistic manner according to the characteristic condition including the regional characteristic. Therefore, it has become possible to create data that can predict the air-conditioning-related energy consumption in a state more suitable for the target house.

[Invention according to Claim 5] The characteristic configuration of the data creating apparatus for calculating energy consumption of a house according to Claim 5 is provided with at-home characteristic analogy means for analogizing the at-home characteristics, and the in-home property analogy is provided. Based on the measured data related to kitchen gas consumption or the measured data related to energy consumption for hot water supply, the means determines whether or not energy consumption for the kitchen or hot water supply is present, and when there is energy consumption for the kitchen or hot water supply, It is configured to infer that they are at home and when they are not at home. According to the data generating device for calculating the energy consumption of the house according to claim 5, the at-home characteristic analogy means is used to calculate, based on the measured data related to the gas consumption for the kitchen or the measured data related to the energy consumption for hot water supply,
Determine whether energy consumption for kitchen or hot water supply is present, and if there is energy consumption for kitchen or hot water supply, stay at home,
When not present, the in-home characteristics can be obtained by analogy with absence. In other words, energy consumption for the kitchen or hot water supply does not normally occur unless there are residents living in the house, so if energy consumption for the kitchen or hot water supply occurs, stay home at that time. It is possible to infer the at-home characteristic based on the energy consumption for the kitchen or hot water supply. Therefore, as the energy consumption measurement data, it is not necessary to purposely obtain the information related to the in-home characteristics of the house from which the measurement data was obtained. In other words, it is not necessary to bother to obtain the information related to the at-home characteristics as the energy consumption measurement data, and the work of creating data for predicting the energy consumption of a house can be simplified.

[Invention of Claim 6] The energy consumption calculation data creating device for a house according to Claim 6 is characterized in that a consumption characteristic analogy means for analogizing the energy consumption characteristic is provided. The analogy means is based on each of the measured data related to the gas consumption for the kitchen, the measured data related to the energy consumption for hot water supply and the measured data for the electric power consumption, based on each of the measured gas consumption for the kitchen, the energy consumption for hot water supply and the electric power consumption. It is configured to infer the energy consumption characteristics of. According to the data generating device for calculating energy consumption of a house according to claim 6, the consumption characteristic analogy means is used to measure measured data related to gas consumption for kitchen, measured data related to energy consumption for hot water supply, and measured actual power consumption. Based on each of the data, the energy consumption characteristics of the kitchen gas consumption, the hot water supply energy consumption, and the electric power consumption are inferred according to the amount of consumption. In other words, based on the actual measurement data related to kitchen gas consumption, the actual measurement data related to energy consumption for hot water supply, and the actual measurement data for general power consumption, the energy consumption characteristics of kitchen gas consumption, energy consumption for hot water supply, and general power consumption Therefore, it is not necessary to purposely obtain the information related to the energy consumption characteristics of the house that has obtained the measured data as the measured data related to energy consumption. Therefore, it is not necessary to bother to obtain the information related to the energy consumption characteristics as the energy consumption measurement data, and the work of creating the data for predicting the energy consumption of the house can be simplified.

[Invention according to claim 7] The characteristic configuration of the data creating device for calculating energy consumption of a house according to claim 7 is that the input means is provided with a gas consumption amount and a time as the energy consumption related measurement data. The gas consumption measurement data indicating the relationship of is input, pre-processing means for performing a pre-processing to separate the gas consumption amount for the kitchen from the gas consumption measurement data input to the input means is provided, the classification means,
The pretreatment means is configured to classify the kitchen gas consumption separated for the kitchen by the pretreatment means as the measurement data related to the kitchen gas consumption based on the classification conditions, and the pretreatment means measures the gas consumption. In the data, the data portion in the gas consumption time zone shorter than the set time is provisionally determined as the kitchen gas consumption amount, and the data portion in the gas consumption time zone longer than the set time is provisionally determined as the heating provisional determination data portion. Then, when the time of the data part tentatively determined as the kitchen gas consumption corresponds to the kitchen working time, the data part is determined as the kitchen gas consumption and does not correspond to the kitchen working time. In that case, the data portion is determined as the heating gas consumption amount, and the minimum gas consumption amount in the heating provisional determination data portion is determined from the heating provisional determination data portion. In the data obtained by subtracting the minimum gas consumption amount from the data portion, the data portion of the gas consumption time period shorter than the set time is determined as the kitchen gas consumption amount, and the data portion of the gas consumption time period of the set time or more is In some cases, the minimum gas consumption amount in the data portion is determined from the data portion and the minimum gas consumption amount is reduced, and in the obtained data, the data portion in the gas consumption time period shorter than the set time is set as the kitchen gas. The operation for determining the consumption amount is configured to be repeated until all the gas consumption time periods are shorter than the set time. According to the data creation device for calculating energy consumption of a house according to claim 7, the gas consumption measurement data indicating the relationship between the gas consumption and time is input to the input means as the energy consumption measurement data, and the preprocessing is performed. The gas consumption amount for the kitchen is obtained by the means based on the gas consumption measurement data input to the input means, and the gas consumption amount for the kitchen obtained by the pretreatment means is actually measured by the classification means in relation to the gas consumption for the kitchen. The data is classified based on the classification conditions. In other words, kitchen gas consumption has features such as shorter gas usage time, large fluctuations in gas consumption, and concentrated gas consumption times when compared to heating gas consumption.
Based on these characteristics, the kitchen gas consumption amount can be obtained by separating the kitchen gas consumption amount from the gas consumption actual measurement data as follows.
That is, the set time is set to a predetermined threshold value between the gas consumption time for the kitchen and the gas consumption time for the heating. Then, in the measured gas consumption data, the data portion in the gas consumption time zone shorter than the set time is provisionally determined as the gas consumption for the kitchen, and the data portion in the gas consumption time zone longer than the set time is provisionally determined for heating. Is tentatively determined as. continue,
If the time of the data portion provisionally determined to be the kitchen gas consumption corresponds to the kitchen working time, the data portion is determined as the kitchen gas consumption, and if it does not correspond to the kitchen working time, the data is determined. The part is determined as the heating gas consumption.
Next, determine the minimum gas consumption in the heating temporary determination data part from the heating temporary determination data part, and in the data obtained by subtracting the minimum gas consumption from the heating temporary determination data part, gas consumption shorter than the set time The data part of the time zone is determined as the gas consumption for the kitchen, and if there is a data part of the gas consumption time zone that is longer than the set time, the minimum gas consumption of that data part is determined from that data part and the minimum gas consumption is determined. The operation of reducing the consumption amount and determining the data portion of the gas consumption time period shorter than the set time in the obtained data as the kitchen gas consumption amount is repeated until all the gas consumption time periods become shorter than the set time, The data portion of the gas consumption time period shorter than the set time is determined as the kitchen gas consumption amount. By the way, in the section over the set gas consumption set corresponding to the minimum input of the gas equipment, the gas consumption of the section where the gas consumption is the smallest among the sections where the change in the gas consumption is substantially constant, Determined to be the minimum gas consumption. In this product, the gas consumption for the kitchen can be calculated based on the gas consumption measurement data obtained by collectively measuring the gas consumption for the kitchen and the gas consumption for the heating. There is no need to measure consumption separately. In other words, the gas consumption for the kitchen can be calculated based on the gas consumption measurement data obtained by collectively measuring the gas consumption for the kitchen and the gas consumption for the heating. It has become possible to simplify the work of creating data.

[Invention according to claim 8] The characteristic construction of the data creating device for calculating energy consumption of a house according to claim 8 is that the hot water supply device is used as the measured data related to the energy consumption for hot water supply in the input means. The amount of hot water supplied from the hot water supply device and the amount of hot water supplied from the hot water supply device to be mixed with the hot water supplied from the hot water supply device Based on the measured data of the water supply amount and the water supply temperature, the sum of the hot water supply amount and the water supply amount is obtained as the hot water consumption amount, and the product of the hot water supply amount and the hot water supply temperature and the water supply amount. Pretreatment means is provided for performing a pretreatment for obtaining the sum of the product of the water supply temperature and the heat quantity of hot water, and the classification means is provided with the hot water consumption amount and the hot water heat quantity obtained by the pretreatment means. The above In that it is configured to classify each class condition. According to the data generating device for energy consumption calculation of a house according to claim 8, the amount of hot water supplied from the hot water supply device, the hot water supply temperature from the hot water supply device, and the hot water supply device from the hot water supply device are used as the actual measurement data related to the energy consumption for hot water supply by the input means. The water supply amount and the water supply temperature of the water to be mixed with the hot water are input, and the hot water supply amount is based on the measured data of the hot water supply amount, the hot water supply temperature, the water supply amount, and the water supply temperature input by the preprocessing unit. And the amount of water supply are added as the amount of hot water consumed, and the product of the amount of hot water supplied and the temperature of hot water and the product of the amount of water supplied and the temperature of water supplied are added as the amount of hot water. The classifying unit classifies the hot water consumption amount and the hot water heat amount obtained by the preprocessing unit for each classification condition. In other words, conventionally, the energy consumption of hot water supply is treated as either “hot water heat load (heat amount: J)” or “hot water supply consumption amount (volume: L)”. Since it is affected by the temperature and the water supply temperature that varies depending on the region and season, when predicting the energy consumption for hot water supply based on the "hot water heat load" or "hot water consumption", It was not possible to predict the energy consumption in a suitable state for the house. Therefore, as the actual measurement data related to energy consumption for hot water supply, the "hot water consumption (volume: L)" and "hot water heat amount (heat amount: J)" obtained by preprocessing as described above are recorded in the storage means. If it is set, it can be converted into a hot water supply heat load, etc., based on the water supply temperature and hot water supply temperature of the target house.
It is possible to predict the energy consumption for hot water supply in a state that is suitable for the target house. Therefore, it has become possible to create data capable of predicting the energy consumption for hot water supply in a state more suitable for the prediction target house.

[Invention according to claim 9] The characteristic configuration of the data creating device for calculating energy consumption of a house according to claim 9 is that the air-conditioning is performed based on the measured data related to the energy consumption for air-conditioning input to the input means. Based on the use condition extraction means for obtaining the use time zone and the set room temperature, and the air conditioning use time zone and the set room temperature obtained by the use condition extraction means,
Energy consumption for air conditioning for calculating the energy consumption for air conditioning by a predetermined heat load calculation program is provided, and the classification means, for each classification condition, the energy consumption for air conditioning calculated by the energy consumption calculation means. It is configured to be classified based on. According to the data generating device for calculating energy consumption of a house according to claim 9, the use condition extracting means obtains the air-conditioning use time zone and the set room temperature from the measured data related to the energy consumption for air-conditioning input to the input means. The energy consumption calculation means calculates the energy consumption for air conditioning by a predetermined heat load calculation program based on the air conditioning use time zone and the set room temperature obtained by the use condition extraction means, and the classification means The energy consumption for air conditioning obtained by the energy consumption calculation means is classified based on each classification condition. In other words, the energy consumption for air conditioning is the main orientation of the house, the arrangement condition of the wife or the middle, the characteristic condition related to the building condition such as the floor, the wall specification, the house area, and the like.
It is easily affected by various types of characteristic conditions such as characteristic conditions related to weather conditions. On the other hand, in order to predict the amount of energy used for air conditioning in a state that suits the houses that are the targets of various predictions, it is necessary to obtain actual measurement data under many conditions that combine various types of characteristic conditions. The work will take a lot of time and effort. Therefore, the air-conditioning usage time zone and the set room temperature are obtained as the measurement data from the measurement data related to the energy consumption for air-conditioning, and the existing general building conditions are also obtained. , A large number of calculation conditions are set by combining building conditions and meteorological conditions obtained in advance. Then, when a building condition and a weather condition are given, a predetermined heat load calculation program capable of calculating the energy consumption for air conditioning required to keep the temperature inside the building constant is used for each of the calculation conditions. Energy consumption for air-conditioning is calculated for each house with various characteristic conditions by calculating the energy consumption for use and recording in the storage means for each classification condition using the calculation condition as a classification condition. Can be asked. Therefore, it is possible to reduce the time and effort required to obtain the actual measurement data related to the energy consumption for air conditioning, and to simplify the work of creating the data for predicting the energy consumption of the house. .

[Invention according to claim 10] The feature of the data creation program for calculating energy consumption of a house according to claim 10 is that a computer for creating data for calculating energy consumption of a house Input means for inputting energy consumption-related measurement data that is consumption-related measurement data, classification means for classifying the energy consumption-related measurement data input to the input means based on predetermined classification conditions, and The energy consumption-related measured data classified by the classification means functions as a storage means for recording each of the classification conditions. According to the house energy consumption calculation data creation program of claim 10, the computer functions as an input unit to input the energy consumption-related measurement data that is the energy consumption-related measurement data of the house. , The computer functions as a classification means,
The energy consumption-related actual measurement data input to the input means is classified based on a predetermined classification condition, and subsequently, the computer functions as a storage means, and the energy consumption-related actual measurement data classified by the classification means is Recorded for each classification condition. That is, the computer is made to function as the input means to input the energy consumption related measurement data. Then, the computer is made to function as the classification means, and the energy consumption-related actual measurement data input to the input means is measured on the basis of the classification condition determined in advance corresponding to the characteristic condition, and the energy consumption-related actual measurement data is measured in the house. Make sure to classify in correspondence with the corresponding characteristic conditions,
Subsequently, the computer is caused to function as a storage unit to record the energy consumption-related actual measurement data classified by the classification unit for each classification condition. Therefore, when predicting the energy consumption of a house, the energy consumption related actual measurement data classified by the classification condition corresponding to the characteristic condition of the prediction target house is read from the storage means, and based on the read data, Since the energy consumption of the house is calculated, the energy consumption of the house can be calculated in a state of being matched with the characteristic conditions of the prediction target house. As a result, it has become possible to provide a data creating program for calculating energy consumption of a house, which can create data capable of predicting energy consumption in a state suitable for the house to be predicted.

[Invention of Claim 11] The feature of the data recording program for calculating energy consumption of a house recorded on the computer-readable recording medium according to claim 11 is that the data for calculating energy consumption of a house is In order to create a computer, input means for inputting energy consumption-related measurement data, which is measurement data related to energy consumption of a house, and the energy consumption-related measurement data input to the input means, according to predetermined classification conditions. The function is to function as a classifying unit that classifies based on the above, and a storage unit that records the energy consumption-related actual measurement data classified by the classifying unit for each of the classification conditions. According to the house energy consumption calculation data creation program recorded on the computer-readable recording medium according to claim 11, the computer, similar to the program according to claim 10, has a computer as an input unit, a classification unit, and Functions as a storage means. That is, since the energy consumption-related actual measurement data is recorded in the storage means for each predetermined classification condition corresponding to the characteristic condition, when predicting the energy consumption of the house, the characteristics of the prediction target house It is possible to read the energy consumption-related actual measurement data classified by the classification condition corresponding to the condition from the storage means, and to calculate the energy consumption of the house based on the read data. It can be obtained in a state that matches the characteristic conditions of the prediction target house. Therefore, it is possible to provide a computer-readable recording medium in which a data creating program for calculating energy consumption of a house, which can create data capable of predicting energy consumption in a state suitable for a house to be predicted, is recorded. I can do it now.

[0017]

BEST MODE FOR CARRYING OUT THE INVENTION A case in which an embodiment of the present invention is applied to create data for predicting energy consumption of an apartment house will be described below with reference to the drawings. Figure 1
Is an energy consumption simulation system for an apartment house (hereinafter, simply referred to as energy The energy consumption simulation system includes an energy consumption calculation device CE (hereinafter, simply referred to as “consumption simulation system”) in addition to the energy consumption calculation data creation device DE. It may be referred to as an energy consumption computing device).

As shown in FIG. 1, the energy consumption simulation system is provided with an arithmetic processing unit 1 constructed by using a microcomputer, a storage unit 2 built in the arithmetic processing unit 1 for recording various information, and various information input. The keyboard 3 and the mouse 4, the reading unit 5 that reads information from the information recording medium M, the display 6 that outputs various information, the printer 7, and the like are provided as main components. The information recording medium M is FD, C
There are various types such as D, DVD, MO, etc., but the reading unit 5
Is provided corresponding to the type of information recording medium M (for example, CD).

In the storage unit 2, a data preparation program unit P1 for executing various processes of the energy consumption calculation data creation device DE, and a consumption energy integration program unit P for executing various processes of the energy consumption calculation device CE.
2 and a program including a recording program unit 100 that records the information processed by the data creation program unit P1 in the storage unit 2 as the database unit B. The information input from the keyboard 3 and the mouse 4, the information read by the reading unit 5, and the storage unit 2 according to the program recorded in
The information recorded in (1) is variously processed, and the processing result is stored in the database section B or output to the display 6 or the printer 7.

Each program of the data creating program part P1, the energy consumption integrating program part P2, and the recording program part 100 is recorded and provided on the information recording medium M, and the reading part 5 reads the programs. , In the storage unit 2.

In short, the data creation program section P1
At, the energy consumption-related actual measurement data of each dwelling unit of the housing complex, which is input from the keyboard 3 or the like, is classified based on a predetermined classification condition, and is recorded as basic data in the database unit B for each classification condition. . Then, the classification condition corresponds to the characteristic condition representing the characteristic of the energy consumption of each house of the housing complex, and the basic data classified based on the classification condition and recorded in the database unit B corresponds to the energy consumption related information, and the consumption Based on the characteristic conditions input from the keyboard 3 and the mouse 4 and the basic data recorded in the database unit B, the energy accumulating program unit P2 calculates the energy consumption amount consumed within the set period for each house. Then, the calculated energy consumption of each house is integrated for all the houses to obtain the total energy consumption of the housing complex, and the calculated total energy consumption is output through the display 6 and the printer 7. still,
The energy consumption-related measurement data includes measurement data of energy consumption in each dwelling unit, and information on characteristic conditions obtained by a questionnaire survey on each dwelling unit.

First, a description will be given of the energy consumption calculation data creation device DE. As shown in FIG. 2, the data creation program part P1 includes a data input part 200,
Basic data organization unit 300 for electricity, kitchen gas, and hot water supply
And an air-conditioning-related basic data organization unit 400. The data input unit 200 is configured to input and process energy consumption-related measured data for a plurality of uses through the keyboard 3 or the like, and the basic data arrangement unit 300 for electricity / gas for kitchen / hot water supply is input to the input unit 200. Measured data related to electricity consumption other than for kitchen, hot water supply, and air conditioning among measured data related to energy consumption for multiple purposes (hereinafter, may be abbreviated as measured data related to electricity)
And measurement data related to kitchen gas consumption (hereinafter sometimes abbreviated as kitchen gas related measurement data) and hot water energy related measurement data (hereinafter sometimes referred to as hot water related measurement data) At home, energy consumption characteristics, resident characteristics (corresponding to family characteristics)
And configured to classify for each classification condition including day characteristics,
The air-conditioning-related basic data organization unit 400 includes a data input unit 2
The air-conditioning energy consumption-related actual measurement data (hereinafter referred to as air-conditioning-related actual measurement data) input in 00 is classified according to classification conditions including building characteristics, day characteristics, and area characteristics.

In the data input section 200, as measured data related to energy consumption for a plurality of uses (corresponding to a plurality of types) of each dwelling unit, measured data of energy consumption are input in a state corresponding to the date and measured. Area of the target housing complex, resident characteristics indicating the family composition of each dwelling unit, building conditions for each dwelling unit (main orientation, arrangement of wife or middle, floor, dwelling unit plan indicating dwelling unit area, etc.), heating and cooling The information obtained by the questionnaire survey about the operating hours and the set temperature is input. The family structure is divided into, for example, a single person, a couple + children, a family whose husband is a worker and his wife is a full-time housewife, a family of dual-workers, and an elderly family. As the gas related measurement data for the kitchen, gas consumption measurement data indicating the relationship between the gas consumption amount and time is input. Further, as the hot water supply related actual measurement data, the amount of hot water supplied from the hot water supply device and the hot water supply temperature, and the amount of water supplied and the temperature of the hot water supplied from the hot water supply device are input in association with time. To be done. Further, as the electricity-related actual measurement data, the power consumption except for hot water supply and air conditioning is input in a state associated with time. Further, as the air-conditioning-related actual measurement data, the energy consumption amount is input in a state associated with time, and the set temperature for air conditioning is input.

The basic data organizing unit 300 relating to electricity, gas for the kitchen and hot water supply will be described. Basically, the basic data organizing unit 300 for electricity, kitchen gas, and hot water supply is
Cut out the measured value of the actual measurement period one day for each dwelling unit, and
It is converted into a form that removes the influence of the region and year,
The way of organizing differs depending on the use of electricity, kitchen gas, and hot water. As shown in FIG. 2 and FIG.
The basic data organizing unit 300 for kitchen gas / hot water supply relates to an actual measurement data pre-processing unit 310 that pre-processes the energy consumption-related actual measurement data input to the data input unit 200, and an at-home process that analogizes the in-home characteristics of the calculation target dwelling unit. Characteristic analogy unit 32
0, the consumption characteristic analogy unit 330 that analogizes the energy consumption characteristic of the dwelling unit to be calculated, the energy consumption-related actual measurement data input to the data input unit 200, or the preprocessing unit 310
The energy consumption related measurement data is pre-processed by the pre-processing data as the organized data, and the classification unit performs the classification process based on the at-home characteristics, energy consumption characteristics, resident characteristics, and day characteristics that are predetermined classification conditions. 340 and 340.

The actual measurement data preprocessing unit 310 executes a kitchen preprocessing for preprocessing the gas related measurement data for the kitchen and a hot water supply preprocessing for preprocessing the hot water supply related actual measurement data. Therefore, the electrical measurement data other than for hot water supply and air conditioning will be used without any preprocessing, but the electrical measurement data other than for hot water supply and air conditioning will not be affected by the season or region. The data can be used as is.

The pretreatment for the kitchen will be described. The kitchen pretreatment is based on the fact that the kitchen gas consumption has the following features when compared to the heating gas consumption.・ While gas is used for a short period of time, it is used for a long period of time throughout the day, while kitchen is often used intermittently for a short period of time.・ Gas consumption fluctuates greatly. Heating equipment is relatively stable, while kitchen equipment has large fluctuations during use.・ The centralized heating time distribution is used for a long time over a wide time zone in the day, while the kitchen heating tends to be concentrated in a certain time zone during the day.

Based on the above characteristics of kitchen gas consumption, in the following steps 1 to 4, the kitchen gas consumption amount is separated from the gas consumption measurement data. Therefore, the gas consumption measurement data includes the gas consumption amounts for the kitchen and the heating, and does not include the gas consumption other than for the kitchen and the heating. Step 1: Noise elimination by moving average A few-order moving average is applied to the measured gas consumption data to eliminate short-term fluctuations contained in the measured value.

Step 2; Provisional determination of individual consumption Assuming that each consumption is for a single kitchen / heating,
Make a provisional decision on the action. Temporary classification of individual gas consumption that is continuous in time is made based on the findings in the non-heating period regarding gas consumption for the kitchen. That is, when the usage time is shorter than the set time ts which is the threshold value between the kitchen and the heating, it is for the kitchen,
If it is long, it is temporarily determined to be for heating. Set time t
s is set to about 1 hour, for example.

Step 3: Determining the use of the action provisionally determined to be for the kitchen If the gas use time provisionally determined to be for the kitchen shows the characteristics of the kitchen working time, it is determined to be for the kitchen. Others are judged to be for heating. Step 4; Separation and Confirmation of Heating Temporary Judgment Data Part Temporarily Determined for Heating As shown in FIGS. 10A to 10C, the heating temporary judgment data part includes only heating. And, there are some that overlap for heating and kitchen. First, from the time variation of consumption in the heating provisional determination data portion, if the variation is small, it is determined as heating only. If the fluctuation is large, it is determined that the heating and the kitchen are mixed, and the individual consumption is separated from the overlapping consumption by the following method. First, it is assumed that the heating capacity is almost constant and that the kitchen is mounted on it. The measured gas consumption is the total for the kitchen and the heating, as shown in FIG. As shown in (D) of FIG. 10, the minimum gas consumption amount of the gas consumption amount within this gas use time is determined and subtracted from the whole. As shown in FIG. 10E, this operation divides the continuous gas use time into a plurality of gas use times. Each of the divided gas use times is compared with the set time ts, and if the time is shorter than this, it is regarded as for the kitchen and confirmed, and if longer, it is (F) in FIG.
As shown in, the operation of determining and reducing the minimum gas consumption amount again is repeated, and when all the gas usage times are equal to or less than the set time ts, the operation is ended. In addition, in the section above the set gas consumption set corresponding to the minimum input of the gas equipment,
The gas consumption amount of the section where the gas consumption amount is the smallest among the sections where the change in the gas consumption amount is substantially constant is determined as the minimum gas consumption amount. In FIG. 10, t1 and t3 are t
s or less, and t2 is longer than ts.

That is, the actual measurement data preprocessing unit 310 tentatively determines the data portion in the gas consumption time zone shorter than the set time ts in the gas consumption actual measurement data as the kitchen gas consumption amount, and determines the gas longer than the set time ts. The data portion of the consumption time zone is provisionally determined as the provisional determination data portion for heating, and then,
If the time of the data portion provisionally determined to be the kitchen gas consumption corresponds to the kitchen working time, the data portion is determined as the kitchen gas consumption, and if it does not correspond to the kitchen working time, the data is determined. The portion is determined as the heating gas consumption amount, and the minimum gas consumption amount in the heating provisional determination data portion is determined from the heating provisional determination data portion, and the minimum gas consumption amount is determined from the heating provisional determination data portion. In the subtracted data, the data portion of the gas consumption time period shorter than the set time ts is determined as the kitchen gas consumption amount, and if there is the data portion of the gas consumption time period longer than the set time ts, the data portion The minimum gas consumption amount in the data portion is determined and the minimum gas consumption amount is reduced. In the obtained data, the data portion in the gas consumption time period shorter than the set time ts is set as the kitchen gas. The operation of determining the cost amount, and are configured to repeat until all of the gas consumption time zone is shorter than the set time ts.

Next, the pretreatment for hot water supply will be described. As shown in FIG. 11, it is determined whether hot water is consumed or not based on the measured data of hot water supplied at every time and the measured data of hot water supplied from the hot water supply device. The water consumption amount V _yu and the amount of hot water H _yu are calculated by the mathematical formula shown in the following Equation 1 with the consumption of water supply as one hot water consumption.

[0032]

[ _Formula 1] V _yu = V _water + V _hot H _yu = V _water × T _water + V _hot × T _hot

However, V _yu : Hot water consumption (L) H _yu : Hot water heat (kJ) V _water , V _hot : Water supply amount, hot water supply amount (L) T _water , T _hot : Water supply temperature, hot water supply temperature

That is, the measured data preprocessing unit 310 obtains the sum of the hot water supply amount and the water supply amount as the hot water consumption amount based on the measured data of the hot water supply amount, the hot water supply temperature, the water supply amount, and the water supply temperature, and It is configured to execute a pre-process for obtaining the product of the hot water supply amount and the hot water supply temperature and the product of the water supply amount and the hot water supply temperature as the hot water heat amount.

The in-home characteristic analogy unit 320 will be further described. The in-home characteristic analogy unit 320 analogizes the in-home situation from the consumption energy generation situation and determines the in-home situation on the actual measurement date of the actual measurement target dwelling unit. As an analogy method, hot water and kitchen gas, which do not occur unless there is a person at home, are used. If these consumptions occur, it is assumed that they are at home at that time, and if they are not consumed for a long time, they are absent. In the present embodiment, as shown in FIG. 12, the in-home characteristics are inferred into six types A to F described later.

The consumption characteristic analogy unit 330 will be further described. As shown in FIG. 13, the consumption characteristic analogy unit 330 is based on the daily total amount / month total amount of energy consumption of each use, and based on the literature value, as shown in FIG. 13, from the high consumption type to the low consumption type. The consumption characteristics of. In other words, the analogy is 4 levels for electricity other than hot water supply and air conditioning, 3 levels for kitchen gas, and 3 levels for hot water supply.

The classification section 340 will be further described. When the input data is the kitchen gas-related measurement data, the classification unit 340 uses the kitchen gas consumption amount separated by the measurement data preprocessing unit 310 as basic data, the home characteristic, the energy consumption characteristic, the resident characteristic, and the resident characteristic. Classification processing is performed based on the day characteristics. Specifically, the basic data of the time variation of the gas consumption for the kitchen (for example, at 15-minute intervals throughout the day) is used to calculate the at-home characteristics,
Label by energy consumption characteristics, resident characteristics and day characteristics.

When the input data is the hot water supply related actual measurement data, the hot water consumption amount and the hot water heat amount obtained by the actual measurement data pre-processing unit 310 are used as basic data for the at-home characteristic, the energy consumption characteristic, the resident characteristic and the daily characteristic. Based on the classification process. Specifically, the basic data of hot water consumption and time variation of hot water heat (for example, at 15-minute intervals throughout the day) are used to calculate the at-home characteristics,
Label by energy consumption characteristics, resident characteristics and day characteristics. When the input data is electrical measurement data,
Electric power consumption data, which is actual measurement data, is used as basic data for home characteristics, energy consumption characteristics,
Classify based on tenant characteristics and day characteristics. Specifically, the time fluctuation of the power consumption (for example, 1
The basic data of (daytime) is labeled by home characteristics, energy consumption characteristics, resident characteristics, and day characteristics.

As mentioned above, the gas-related measurement data for the kitchen,
Classification section 3 for hot water supply related measurement data and electricity related measurement data
As the day characteristic, which is one of the classification conditions for classification in 40, the season (for example, three conditions of spring, summer, and winter) and the day of the week (for example, holiday and weekday 2) derived from the measurement date of the actual measurement data are used. Conditions), and the measurement date of the actual measurement data and the weather (for example, three conditions of fine weather, cloudy weather, and rain) derived from the observation data of the weather station.

The basic data rearranging unit 400 related to air conditioning will be described. As shown in FIG. 2 and FIG. 4, the air-conditioning-related basic data organizing unit 400 extracts from the air-conditioning-related actual measurement data an air-conditioning condition including a time zone for performing air-conditioning (heating / cooling) and a set temperature. And a calculation condition organizing unit 420 that creates a combination of the calculation conditions based on the air conditioning conditions extracted by the use condition extracting unit 410, the building conditions and the weather conditions that are considered to be general, and the calculation condition organizing unit 420. For each calculation condition created by
Energy consumption calculation unit 430 for obtaining the energy consumption for air conditioning by a predetermined heat load calculation program, and the energy consumption for air conditioning for each calculation condition obtained by the energy consumption calculation unit 430 as basic data for each calculation condition And a classification unit 440 for classifying as a classification condition. In addition, the building conditions (including wall specifications, plans, fittings, windows, and layout conditions) that are considered to be general are extracted and arranged in advance from the existing building as the information necessary for the basic data reduction process of the actual measurement data related to air conditioning. The general building conditions are stored in the database section B, and the weather data of the date and time of the calculation target area are also stored in the database section B.

[0041] The addition of described calculation condition organizing unit 420, the calculation condition organizing unit 420, calculation target dwelling unit plans as building conditions (e.g., 60 m ^2, 80 m ^2, 100
m ² ), housing arrangement conditions (main direction, arrangement of wife or middle, floor), air conditioner use time pattern (weekdays), air conditioner use time pattern (holiday), air conditioner set temperature (m ² ). For example, the calculation condition is created by a combination of 26 ° C, 28 ° C, 30 ° C), the calculation target area and the calculation target year. The number of combinations is as shown in the following mathematical formula 2.

[0042]

## EQU2 ## C _num = Rplan × Rpos × AS _wday × A
_Swend x RT x Rg x Y

C: number of condition combinations Rplan: number of dwelling unit plans to be calculated Rpos: number of dwelling unit arrangement conditions AS _wday : number of air-conditioning time zone patterns (weekdays) AS _wend : number of air-conditioning time zone patterns (holiday) RT: air conditioning Set temperature Rg: Number of calculation target areas Y: Number of calculation target years

The energy consumption calculation unit 430 will be further described. The energy consumption calculation unit 430 calculates the energy consumption of the air conditioning equipment for each calculation condition created by the calculation condition arrangement unit 420 by a predetermined heat load calculation program. As a predetermined heat load calculation program,
For example, a residential heat load calculation program BRIMAP
(Refer to "Housing and Building Energy Saving Mechanism"). In other words, a wide range of studies have been conducted over a long period of time regarding the calculation method of energy (heat load) consumed to keep the indoor temperature inside the building constant when the building conditions and the external weather conditions are given. , The theory and solution have already been established, so we will only briefly explain the outline of indoor heat load calculation.

The amount of heat flowing from the room j to the room i between the adjacent rooms in the building is represented by the following mathematical formula.
Almost the same formula is used for the part facing the outside air.

[0046]

[Equation 3]

Since the above equations are used for each room in the building, the problem is a simultaneous equation regarding the room temperature of each room. The room temperature of each room is calculated by solving this simultaneous equation by the SOR method or the like. In a room where air conditioning is performed at the calculation target time, the room temperature is determined according to the room temperature setting condition, and the heat quantity required to maintain the room temperature at the calculation target time, that is, the air conditioning heat load is calculated. However, when this air conditioning heat load exceeds the capacity of the air conditioner, the room temperature is calculated again assuming that only the capacity of the air conditioner has been processed. The room temperature (natural room temperature) is calculated in a room that is not air-conditioned at the calculation target time.

If the classification unit 440 is further described, the classification unit 440 uses the air-conditioning energy consumption calculated by the energy consumption calculation unit 430 as basic data to perform classification processing based on classification conditions (corresponding to calculation conditions). To do. Specifically, the basic data of the time variation of the energy consumption for air conditioning (for example, every 15 minutes during the day) is labeled under the classification condition. In other words, classification conditions include building characteristics (including housing unit plans and layout conditions), air conditioning conditions (set temperature,
Includes air conditioning usage time), day characteristics (including weekdays and holidays), and regional characteristics.

The recording program section 100 will be described below. The recording program section 100 includes the classification section 34 of the basic data organization section 300 for electric / kitchen gas / hot water supply.
The basic data classified by 0 and the basic data classified by the classification unit 440 of the air-conditioning-related basic data organizing unit 400 are recorded in the database unit B in a state of being classified for each classification condition.

That is, the actual measurement data itself, the data obtained by processing the actual measurement data by the actual measurement data preprocessing section 310, and the actual measurement data are used as the energy consumption-related actual measurement data classified by the classification sections 340 and 440. The data processed by the unit 410 and the energy consumption calculation unit 430 are included.

Next, the processing procedure of the entire data creation program section P1 will be described with reference to the flowcharts shown in FIGS. As shown in FIG.
In the data input section 200, the gas related measurement data for kitchen,
The hot water supply-related measurement data, the electricity-related measurement data, and the air-conditioning-related measurement data are input separately, and when the kitchen gas-related measurement data, the hot-water supply-related measurement data, and the electricity-related measurement data are input, the electricity / kitchen A basic data rearranging process for classifying input data for each classification condition is executed by the basic data rearranging unit 300 for gas / hot water supply, and the basic data after the classification process is stored in the database unit B in the recording program unit 100. When the recording process for recording each time is executed and the air-conditioning related actual measurement data is input, the basic data rearranging process for classifying the input data by the classification condition by the air-conditioning related basic data rearranging unit 400 is executed to perform the classification process. The recording program unit 100 executes a recording process of recording the generated basic data in the database unit B for each classification condition.

As shown in FIG. 6, the basic data reduction processing of the kitchen gas-related measurement data is performed by the measurement data preprocessing unit 31.
At 0, pre-processing for separating the gas consumption for the kitchen from the measured data of gas consumption is executed, and then the in-home characteristic analogy unit 320
, Analogize the at-home characteristic based on the measured gas consumption data, then the consumption characteristic analogy unit 330 estimates the energy consumption characteristic based on the measured gas consumption data, and then inputs it together with the measured gas consumption data. The day characteristic derivation process for calculating the day characteristic based on the measured date is executed, and then,
The classifying unit 340 performs a classifying process for classifying on the basis of the at-home characteristic, the energy consumption characteristic, the resident characteristic, and the day characteristic with the kitchen gas consumption amount as basic data.

As shown in FIG. 7, the basic data reduction processing of the hot water supply related actual measurement data is performed by the actual measurement data preprocessing unit 310 based on the actual measurement data of the water supply at each time and the actual measurement data of the hot water supply from the hot water supply device. Pre-processing for obtaining the consumption amount and the amount of heat of hot water is executed, then the at-home characteristic analogy unit 320 analogizes the at-home property based on the gas consumption measurement data, and then the consumption property analogy unit 330 uses the hot water. The energy consumption characteristic is analogized based on the consumption amount, and subsequently, the day characteristic deriving process for obtaining the day characteristic based on the measurement date input together with the actual measurement data is executed. Using the amount of heat and the amount of heat of hot water as basic data, classification processing is performed based on at-home characteristics, energy consumption characteristics, resident characteristics, and day characteristics.

As shown in FIG. 8, in the basic data reduction process of the electricity-related measured data, the in-home characteristic analogy unit 320 analogizes the in-home property based on the gas consumption measured data, and then the consumption property analogy unit 330. In, the analogy of the energy consumption characteristics based on the data of the power consumption, followed by performing the day characteristics derivation process for obtaining the day characteristics based on the measurement date input together with the actual measurement data, then, the classification unit At 340, using the power consumption data itself as basic data,
Classification processing is performed based on at-home characteristics, energy consumption characteristics, resident characteristics, and day characteristics.

As shown in FIG. 9, the basic data reduction processing of the air-conditioning related actual measurement data is performed by the use condition extracting section 410 including the actual measurement data and the set temperature of the air-conditioning with the elapsed energy consumption of heating and cooling. An air-conditioning condition extraction process is executed to extract air-conditioning conditions including the time zone for air-conditioning and the set temperature from the related actual measurement data. Next, the general building conditions are read out, and then the calculation condition arrangement unit 420 calculates the calculation target dwelling unit plan, the dwelling unit arrangement conditions, the air-conditioning use time zone pattern (weekdays), the air-conditioning use time zone pattern (holiday), Execute the calculation condition arrangement process that arranges the calculation conditions according to the combination of the set temperature of the air conditioning, the calculation target area, and the calculation target year. Subsequently, the energy consumption calculation unit 430 executes the energy consumption-calculation process of calculating the energy consumption of the air conditioner on one day of the calculation target while reading out the weather conditions of the calculation target region / date and time. When the calculation for the day ends, the calculation of the energy consumption of the air conditioner for one day of the next calculation target day is repeated until the calculation target period ends, and when the calculation target period ends, the classification unit 440
At, a classification process for classifying each calculation condition as a classification condition is executed. The basic data reduction processing of the above-mentioned air-conditioning related actual measurement data is executed for all combinations of target areas and calculation conditions.

Next, the energy consumption computing device CE will be described. As shown in FIG. 2 and FIG. 14, the energy consumption integration program unit P2 that executes various processes of the energy consumption computing device CE includes a condition input unit 50.
0, dwelling unit characteristic assigning unit 600, usage-specific energy consumption integrating unit 700, calculation result organizing unit 800, and output unit 900
It consists of and.

The condition input unit 500 is configured to input the characteristic conditions input through the keyboard 3 and the mouse 4, and the dwelling unit characteristic assigning unit 600 includes the condition input unit 500.
The characteristic conditions input by the method are configured to be assigned to each dwelling unit of the housing complex to be calculated, and the energy consumption integrating unit 700 for each purpose is based on the characteristic condition assigned by the dwelling unit characteristic assigning unit 600 for each dwelling unit. Database section B for each energy use (corresponding to the type of energy)
From the above, select the basic data that meets the characteristic conditions for each dwelling unit and accumulate it for each dwelling unit to obtain the energy consumption amount consumed within the set period. Then, it is configured to calculate the total energy consumption of the housing complex, and the calculation result organizing unit 80
0 is configured to variously organize the total energy consumption amount for each energy use obtained by the energy consumption integrating unit 700 for each application, and the output unit 900 is arranged by the calculation result organizing unit 800. The generated data is output to the display 6 and the printer 7.

The condition input section 500 will be described with reference to FIG. The condition input unit 500 is composed of an essential condition input unit 510 and a detailed condition input unit 520 in order to reduce the burden on the input person. Mandatory condition input section 510
Executes the input process of the characteristic condition that is indispensable for the cost estimation and can be easily checked for each apartment house. The enterer must enter all characteristic conditions. The detailed condition input unit 520 executes an input process of an important condition such as the in-home situation and energy consumption characteristics of the inhabitants, which cannot be easily checked by the input person.

The indispensable condition input section 510 is a calculation target year input section 511 for inputting a calculation target year, a calculation area input section 512 for inputting a calculation target area, and a housing unit number input section 513 for inputting the number of dwelling units of a calculation target housing complex. , Occupancy rate input section 514 that inputs the ratio of dwelling units in which residents actually live in the calculation target housing complex, dwelling unit conditions consisting of dwelling unit plan (floor area), main direction, and floor of each dwelling unit in the calculation target housing complex For each combination, the housing unit condition input unit 515 for inputting the number of housing units corresponding to each combination, and the family characteristic input unit for inputting the family characteristics of the residents expected to move in by the ratio to the total number of housing units in the apartment by each family characteristic 516 and the like.

The detailed condition input section 520 indicates the in-home characteristic input section 521 for inputting the daily in-home characteristics of the entering and leaving units, the heating characteristics of each dwelling unit (usage period throughout the year, each use time zone on weekday holidays and set temperature). Heating condition input section 52 for input
2. Cooling condition input section 523 for inputting cooling characteristics of each dwelling unit (usage period throughout the year, each use time zone on weekdays and holidays, and set temperature), and energy consumption characteristics for electric energy consumption other than for hot water supply and air conditioning And an electric condition input unit 525 for inputting energy consumption characteristics in hot water supply, and a kitchen gas condition input unit 526 for inputting energy consumption characteristics in kitchen gas.

The condition input section 500 displays a basic input screen as shown in FIG. 26 on the display 6, and executes each input processing by this basic input screen. The calculation target year input unit 511, the calculation area input unit 512, the number of dwelling units input unit 513, and the occupancy rate input unit 514 each execute data input processing on this basic input screen.

The dwelling unit condition input section 515, when the "set dwelling unit layout" button is clicked on the basic input screen,
A dwelling unit condition input screen (not shown) is displayed on the display 6, and the number of dwelling units is input for each dwelling unit condition combination consisting of a dwelling unit plan (floor area) and main azimuth / floor arrangement conditions. That is, the dwelling unit condition input unit 515 inputs the dwelling unit plan and arrangement condition allocation conditions as the dwelling unit characteristic allocation conditions for assigning characteristic conditions to each dwelling unit.

The family characteristic input section 516 displays a family structure input screen (not shown) on the display 6 when the "family structure input" button is clicked on the basic input screen to display "single person", "married couple". A process of inputting the number of housing units corresponding to each family characteristic such as "+ wife is full-time housewife", "married couple + children working together", and "elderly family" is executed as a ratio to the total number of moving-in units. That is, the family characteristic input unit 516 includes family characteristics such as "single person", "married couple + child wife is full-time housewife", "couple / children working together", and "elderly family" as the dwelling unit characteristic allocation conditions. The conditions for assigning family characteristics are entered.

When the "at-home status setting" button is clicked on the family configuration input screen, the at-home characteristic input unit 521 displays a at-home status input screen (not shown) on the display 6 for weekdays and holidays. , "All day stay", "Short day out", "Long day out",
"Absent in the afternoon", "Absent in the morning" and "Absent all day" A ~
A process of inputting the number of dwelling units corresponding to each of the six patterns of F as a ratio to the total number of dwelling units is executed. That is, the at-home characteristic input unit 521 inputs the at-home characteristic allocation conditions consisting of six patterns A to F for weekday and holiday as the dwelling unit characteristic allocation conditions.

When the "heating-related" button is clicked on the basic input screen, the heating condition input unit 522 displays a heating usage status input screen (not shown) on the display 6 to indicate the heating usage period throughout the year. As you type
For weekdays and holidays, a process of inputting the number of dwelling units corresponding to each use time zone and the number of dwelling units corresponding to each set temperature as a ratio to the total number of dwelling units is executed. That is, the heating condition input unit 522 sets
The allocation condition of the heating condition (corresponding to the air conditioning condition) consisting of each use time zone on weekdays and holidays and the set temperature is input.
When the “cooling-related” button is clicked on the basic input screen, the cooling condition input unit 523 displays a cooling usage status input screen (not shown) on the display 6 and inputs the period of use of cooling throughout the year. , The number of dwelling units corresponding to each use time zone and the number of dwelling units corresponding to each set temperature are input as a ratio to the total number of dwelling units for each of weekdays and holidays. That is, the cooling condition input unit 52
As a result, the allocation condition of the cooling condition (corresponding to the air conditioning condition) consisting of each use time zone on weekdays and holidays and the set temperature is input as the housing unit characteristic allocation condition.

When the "electrical-related" button is clicked on the basic input screen, the electrical condition input section 524 displays an electrical-related energy consumption characteristic input screen (not shown) on the display 6 to display "multi-consumption type", "Semi-consumable",
A process of inputting the number of dwelling units corresponding to the electric energy consumption characteristics of each of the "standard type" and the "saving type" as a ratio to the total number of dwelling units is executed. That is, the electrical condition input unit 5
24, the allocation conditions of the electrical energy consumption characteristics of “high consumption type”, “semi-high consumption type”, “standard type” and “saving type” are input as the housing unit characteristic assignment conditions.

When the "hot water supply related" button is clicked on the basic input screen, the hot water supply condition input unit 525 displays a hot water supply related energy consumption characteristic input screen (not shown) on the display 6 to display "high consumption type", A process of inputting the number of dwelling units corresponding to the energy consumption characteristics of each of the "standard type" and the "saving type" as a ratio to the total number of dwelling units is executed.
That is, the hot water supply condition input unit 525 inputs the hot water supply energy consumption property allocation conditions of “high consumption type”, “standard type” and “saving type” as the dwelling unit property allocation conditions.

When the "kitchen-related" button is clicked on the basic input screen, the kitchen gas condition input unit 526 displays a kitchen-related energy consumption characteristic input screen (not shown) on the display 6 to indicate "high consumption type". , The "standard type" and the "conservation type" are respectively inputted as the number of dwelling units corresponding to the energy consumption characteristics with respect to the total number of dwelling units. That is, the kitchen gas condition input unit 526 inputs the kitchen gas consumption characteristic allocation conditions of “high consumption type”, “standard type” and “saving type” as the dwelling unit characteristic allocation conditions.

The description of the dwelling unit characteristic assigning unit 600 will be added. The dwelling unit characteristic assigning unit 600 includes a condition input unit 50.
Based on the dwelling unit characteristic allocation conditions input in 0, for each dwelling unit, the dwelling unit plan, layout condition, family characteristic, at-home characteristic, heating condition, cooling condition, electric energy consumption characteristic, hot water energy consumption characteristic and kitchen gas consumption characteristic Assign each assignment condition. In the condition input unit 500, the allocation condition for each item is input as a ratio to the total number of dwelling units that fit each type, and this dwelling unit characteristic allocation unit 600 uses the ratios for individual dwelling units. One type is assigned to each house by the Monte Carlo method. FIG. 27 shows an example of allocation.

The Monte Carlo method assigns one characteristic value (parameter) according to the probability that a random number is generated by using a computer. Since it is well known, its detailed description will be omitted and a brief description will be given below. Stop doing it. P ₁ · P _{2 to} P _n : Probabilities of occurrence of types 1 to n (0-1) related to characteristics (characteristic allocation condition) Ran: Uniformly distributed random number (take any number between 0 and 1)
Then, at this time, among k that satisfies 1 ≦ k ≦ n, the one that satisfies the following mathematical formula 4 is the type assigned to this dwelling unit (k type).

[0071]

[Equation 4]

The energy consumption integrating unit for each application 700 will be described with reference to FIG. The usage-specific energy consumption integration unit 700 determines the season and day of the week as a day characteristic from the year, month, and day of the calculation target day, and reads the weather condition as a day characteristic related to the weather of the day from the database unit B. , An in-home situation generating unit 720 that determines the in-home situation by the Monte Carlo method according to the in-home and day characteristics of each dwelling unit, an electricity integrating unit 730 that integrates energy consumption related to electricity, a hot water supply integrating unit 740 that integrates energy consumption related to hot water supply, A kitchen gas integration unit 750 that integrates energy consumption related to kitchen gas,
Heating integration unit 76 that integrates energy consumption related to heating
It is configured by a cooling integration unit 770 that integrates 0 and energy consumption related to cooling.

As shown in FIG. 17, the electricity accumulator 730.
First, regarding the calculation target dwelling unit on the calculation target day, first, the condition selection unit 731 sorts out the day characteristic including the day of the week, the season, the weather, the at-home characteristic, and the electric energy consumption characteristic, and conforms to all the characteristic conditions. Basic data is selected from the electrical relations in the database section B. When there are a plurality of basic data that meet the characteristic conditions, the random selection unit 732 randomly selects one basic data based on the Monte Carlo method. The time variation of the power consumption is recorded in the basic data, and this variation is accumulated in the electricity consumption integrating unit 733 over all the units. As a result, it is possible to accumulate the daily characteristics that affect all dwelling units across all dwelling units, and accumulate time fluctuations in power consumption while considering daily home randomness and energy consumption characteristics. Becomes

As shown in FIG. 18, hot water supply integrating section 740.
Regarding the calculation target dwelling unit on the calculation target day, first, the condition selecting unit 741 sorts out the characteristic properties of the day of the week, season, and weather, the at-home property, and the hot water supply energy consumption property, and conforms to all the characteristic conditions. Basic data is selected from hot water supply relations in the database section B. When there are a plurality of basic data that meet the characteristic conditions, the random selection unit 742 randomly selects one basic data based on the Monte Carlo method. In the basic data, time variations of the hot water consumption amount and the hot water heat amount are recorded. Therefore, based on the hot water supply temperature calculated by the hot water supply temperature calculation unit 743, the hot water supply heat load calculation unit 744 calculates the hot water consumption amount and the hot water heat amount. At, the hot water supply heat load that is the actual energy consumption is calculated, and the time variation of the hot water supply heat load is accumulated by the hot water supply energy consumption integrating unit 745 over all the units.

The water supply temperature calculation unit 743 estimates the water supply temperature based on the equation for estimating the water supply temperature from the outside air temperature.
For the formula for estimating the water supply temperature from the outside air temperature, see "Research Committee on Examination and Maintenance of Basic Data for Hot Water Supply Equipment Design".
Proposals have already been made by the Society of Air Conditioning and Sanitary Engineering. There are several forms of the estimation formula, but in the present embodiment, the linear simple regression formula shown in the following Expression 5 in which only the outside air temperature of the day is used as the explanatory variable is used.

[0076]

[Formula 5] T _water = a _i × T _air + b _i

However, T_Water: Estimated water temperature T_air: Outside temperature on the calculation day of the calculation target area a_i, B_i: Coefficient in region I (in Tokyo, a_i=
0.8516, b _i= 2.473)

However, in the housing complex, since water is often stored once in the water tank inside the plant, the water supply temperature tends to be high throughout the year. Therefore, in this embodiment, the temperature rise correction coefficient C _plant for the whole year of the _plant is added, and the mathematical expression shown in the following Expression 6 is used.

[0079]

[Equation 6] T _water = a _i × T _air + b _i + C _plant

Further, the hot water supply heat load calculation unit 744 calculates the hot water supply heat load according to the following mathematical expression.

[0081]

(7) H _load = H _yu − (V _yu × T _water ) × 4.2

However, H _load : Hot water supply heat load [kJ] H _yu : Heat quantity of hot water (kJ) V _yu : Hot water consumption (L)

As shown in FIG. 19, a kitchen gas integrating section 75.
As for 0, regarding the calculation target dwelling unit on the calculation target day, first, the condition selection unit 751 sorts out the characteristic characteristics of the day characteristic including the day of the week, season, and weather, the characteristic of home, and the characteristic of gas consumption in the kitchen, and conforms to all the characteristic conditions. The basic data to be selected is selected from the kitchen gas relation in the database section B. When there are a plurality of basic data that meet the characteristic conditions, the random selection unit 752 randomly selects one basic data based on the Monte Carlo method. The time variation of the kitchen gas consumption amount is recorded in the basic data, and the time variation of the kitchen gas consumption amount is accumulated in the kitchen gas consumption amount accumulating unit 753 for all the dwelling units.

Since the heating integrating unit 760 and the cooling integrating unit 770 are the same, they will be collectively described with reference to FIG.
It is determined whether the calculation target day is within the air conditioning period input by the condition input unit 500, and if it is not the calculation period, the calculation proceeds to the next day. Next, based on the in-home characteristics of the calculation target dwelling unit, if it is an absent day, the calculation moves to the next dwelling unit assuming that energy consumption for air conditioning did not occur on that day. If the calculation target day is during the air conditioning period and the calculation target dwelling unit is absent, it is determined that energy consumption for air conditioning is generated, and the condition selection unit 761, 771
In, selection of basic data is performed. Based on the air-conditioning conditions (air-conditioning use time zone, set room temperature), dwelling unit plan, arrangement conditions, and the area where the calculation-destination housing complex is located, basic data matching the combination of the conditions is selected from the air-conditioning relation of the database B. Unlike other uses, there is only one basic data that matches one condition combination. In the basic data, the calculation result of the air conditioning related energy consumption under a certain condition combination on the calculation target day is recorded in the form of time variation. The selected basic data is accumulated by the air conditioning energy consumption integration units 762 and 772 for all the dwelling units on the calculation target day.

Based on FIG. 21, calculation result organizing unit 800
Will be added. The calculation result organizing unit 800 includes the respective integrating units 73 of the above-described usage-specific energy consumption integrating unit 700.
The time fluctuation of energy consumption in the housing complex calculated at 0, 740, 750, 760, and 770 (15-minute intervals in this embodiment) is summarized and arranged as a useful calculation result. The integration result of the usage-specific energy consumption integration unit 700 takes the form of time variation of the total energy consumption of all dwelling units in the calculation-target housing complex on each day during the calculation period. Therefore, the summation and organizing unit 810 sorts out the summation result of each day as a form of time variation of energy consumption for all purposes. or,
The total / average processing unit 820 sorts out the total / average values of the monthly average time fluctuations and daily total values. The statistical value processing unit 830 organizes statistical values such as average values and peak values required at the time of design.

The output unit 900 includes the respective integrating units 730, 740, 750, and 76 of the energy consumption integrating unit for each application 700.
Time variation of energy consumption in an apartment house calculated at 0,770, time variation of energy consumption for all purposes calculated at the integration and adjustment unit 810, total average processing unit 820
The average value of the monthly averages calculated in the above, the total value / average value such as the daily total value, and the statistical value such as the average value and the peak value calculated by the statistical value processing unit 830 are displayed on the display 6 and the printer 7. Output to. FIG. 28 shows an output example of time variation of energy consumption for all purposes, and FIG. 29 shows an example of change and distribution of daily total value of power consumption and peak value of power consumption.

Next, the processing procedure of the entire energy consumption program section P2 will be described with reference to the flow charts shown in FIGS. FIG. 22
As shown in FIG. 5, the condition input unit 500 executes the characteristic condition input process of inputting the characteristic condition input through the keyboard 3 or the mouse 4, and the housing unit characteristic assignment unit 600 inputs the condition condition input unit 500. Based on the specified conditions for allocating dwelling units, the conditions for allocating dwelling unit plans, layout conditions, family characteristics, at-home characteristics, heating conditions, cooling conditions, electrical energy consumption characteristics, hot water energy consumption characteristics, and kitchen gas consumption characteristics are assigned for each dwelling unit. The dwelling unit characteristic allocation process is executed, and the energy consumption integrating unit 700 for each purpose is executed.
In, the calculation result arrangement unit 800 calculates the result of adding up the consumption energy for each application by executing the application-specific consumption energy integration process of adding up the consumption energy for each application of electricity, hot water supply, kitchen gas, heating and cooling. The result arrangement processing is executed, and the output unit 900 uses the energy consumption integrating unit 7 for each application.
00 or the calculation result of the calculation result organizing unit 800 is output to the display 6 or the printer 7.

As shown in FIG. 23, the characteristic condition input process is an essential condition input process for inputting an essential characteristic condition based on the basic input screen as shown in FIG. 26, and a characteristic condition other than the essential condition is inputted. When the detailed condition input process is executed and the “start calculation” button on the basic input screen as shown in FIG. 26 is clicked, it is determined whether or not all the essential conditions have been input, and all the essential conditions have been input. If yes, return. If not, wait for the rest of the essential conditions to be entered.

As shown in FIG. 24, the dwelling unit characteristic allocation process is performed for each dwelling unit plan, arrangement condition, family characteristic, at-home characteristic, heating condition, cooling condition, electric energy consumption characteristic, hot water supply energy consumption characteristic and kitchen gas consumption characteristic. Of the allocation conditions, the characteristic conditions entered in the characteristic condition input process
The process of assigning all the dwelling units of the calculation target apartment complex is executed. However, vacant dwelling units will not be allocated.

As shown in FIG. 25, in the energy consumption accumulating process for each purpose, the day characteristic generation unit 710 determines the season and day of the week as the day characteristic from the date of the calculation target date from the database unit B and determines the day. A home condition generation process that executes a day characteristic generation process that reads in weather conditions related to the weather of the day as a characteristic and determines the home condition by the Monte Carlo method according to the home characteristic and day characteristic of each dwelling unit in the home condition generation unit 720. The electricity integration unit 730, the hot water supply integration unit 740, the kitchen gas integration unit 750, and the heating integration unit 76 are executed.
0 and the cooling integration unit 770 repeats the process of accumulating daily energy consumption for each use of electricity, hot water supply, kitchen gas, heating, and cooling until it finishes for all entrance units, and when it ends for all entrance units, If all the calculation target periods have not been calculated, move to the next day, repeat the daily energy consumption loop from the day characteristic generation processing again, and if the calculation target period ends, use energy consumption integration processing by application To finish.

In the energy consumption simulation system configured as described above, the input means I for inputting the energy consumption-related measurement data which is the energy consumption-related measurement data of the house is the keyboard 3, the arithmetic processing section 1, and the data creation. It is configured by the data input unit 200 of the program section P1. Further, the classification means C for classifying the energy consumption-related actual measurement data input to the input means I on the basis of a predetermined classification condition is the calculation processing section 1 and the classification section 34 of the data creation program section P1.
It is composed of 0,440. Further, the storage means R for recording the energy consumption-related actual measurement data classified by the classification means C for each classification condition is configured by the arithmetic processing section 1, the recording program section 100, and the storage section 2. Further, the at-home characteristic analogy means H for analogizing at-home characteristics is composed of the arithmetic processing unit 1,
In addition, the at-home characteristic analogy unit 320 of the data creation program unit P1 is configured. Further, the consumption characteristic analogy means S for analogizing the energy consumption characteristic is composed of the arithmetic processing section 1 and the consumption characteristic analogy section 330 of the data creating program section P1. Further, the pre-processing means T includes the arithmetic processing unit 1,
Further, it is configured by the measured data preprocessing unit 310 of the data creation program unit P1. Further, the use condition extracting means A for obtaining the air-conditioning use time zone and the set room temperature from the measured data related to the energy consumption for air-conditioning input to the input means I is
The calculation processing unit 1 and the use condition extraction unit 410 of the data creation program unit P1 are included. Further, the energy consumption calculation means K for obtaining the energy consumption for air conditioning by a predetermined heat load calculation program on the basis of the air conditioning use time zone and the set room temperature obtained by the use condition extraction means A is the calculation processing unit 1, Further, it is configured by the energy consumption calculation unit 430 of the data creation program unit P1.

Another Embodiment Next, another embodiment will be described. (B) In processing the energy consumption-related actual measurement data of a plurality of energy applications (corresponding to the types of energy) in the housing energy consumption calculation data creation device DE of the present invention, the energy applications are the same as in the above embodiment. The application is not limited to the illustrated application, and may be partially omitted from the application illustrated in the above embodiment,
Alternatively, new applications such as bath gas applications may be added. Alternatively, the energy consumption related measurement data for one type of energy use may be processed.
Alternatively, it may be configured to process two data, that is, the total consumption of gas and the total consumption of electricity.

(B) Each of the characteristic condition and the classification condition is
The present invention is not limited to those exemplified in the above embodiment, and may be partially omitted from those exemplified in the above embodiment. In that case, each of the characteristic condition and the classification condition may be one type. Alternatively, new ones such as income group, occupation, location (city center, suburbs) may be added.

(C) In the above embodiment, the pretreatment means T is omitted, and the measurement data of the gas consumption for the kitchen is measured as the measurement data related to the kitchen gas, and the heat load of the hot water supply is measured as the measurement data related to the hot water supply. The data may be input, and the input data themselves may be classified by the classification means C for each classification condition.

(D) In the above embodiment, the case where the present invention is applied to create data for predicting energy consumption of an apartment house has been exemplified. However, data creation for predicting energy consumption of a detached house is created. It can also be applied to

(E) Data creation program part P1 and recording program part 100 for executing various processes of the energy consumption calculation data creation device DE, and energy consumption integration for executing various processes of the energy consumption calculation device CE. The program section P2 may be configured to be downloaded by the arithmetic processing section 1 via the web such as the Internet and recorded in the storage section 2.

(F) In the above-described embodiment, the energy consumption simulation data for the housing of the present invention DE is provided together with the energy consumption calculation device CE for the housing, and the energy consumption simulation for the housing is provided. Although the case where it is provided by the system has been illustrated, it can be supplied by the data generating device DE for calculating the energy consumption of the house alone.

[Brief description of drawings]

FIG. 1 is a block diagram of an energy consumption simulation system for a housing complex including a data creating device for calculating energy consumption of a house according to an embodiment.

FIG. 2 is a diagram illustrating a program configuration of an energy consumption simulation system for an apartment house.

FIG. 3 is a diagram for explaining the configuration of a basic data organizing unit related to electricity / gas for kitchen / hot water supply.

FIG. 4 is a diagram illustrating a configuration of a basic data organization unit related to air conditioning.

FIG. 5 is a diagram showing a flowchart of a data creation program section.

FIG. 6 is a diagram showing a flowchart of a basic data reduction process of gas related measurement data for kitchen.

FIG. 7 is a diagram showing a flowchart of a basic data reduction process of hot water supply related actual measurement data.

FIG. 8 is a diagram showing a flowchart of a basic data reduction process of electric measurement data.

FIG. 9 is a diagram showing a flowchart of a basic data reduction process of actually measured data related to air conditioning.

FIG. 10 is a diagram for explaining kitchen pretreatment.

FIG. 11 is a diagram illustrating hot water supply pretreatment.

FIG. 12 is a diagram illustrating analogy of home characteristics.

FIG. 13 is a diagram illustrating analogy of energy consumption characteristics.

FIG. 14 is a diagram illustrating a configuration of a program unit for integrating energy consumption.

FIG. 15 is a diagram showing a configuration of a condition input unit.

FIG. 16 is a diagram illustrating a configuration of an energy consumption integrating unit for each application.

FIG. 17 is a diagram illustrating a configuration of an electricity integrating unit.

FIG. 18 is a diagram illustrating a configuration of a hot water supply integrating unit.

FIG. 19 is a diagram illustrating a configuration of a kitchen gas integrating unit.

FIG. 20 is a diagram for explaining the configurations of a heating integrating unit and a cooling integrating unit.

FIG. 21 is a diagram illustrating a configuration of a calculation result arrangement unit.

FIG. 22 is a diagram showing a flowchart of a program unit for integrating energy consumption.

FIG. 23 is a diagram showing a flowchart of characteristic condition input processing.

FIG. 24 is a diagram showing a flowchart of dwelling unit characteristic assignment processing.

FIG. 25 is a diagram showing a flowchart of energy consumption integration processing by application.

FIG. 26 is a diagram showing a basic input screen.

FIG. 27 is a diagram of a display screen showing an allocation example.

FIG. 28 is a diagram showing an example of output of calculation results.

FIG. 29 is a diagram showing an output example of calculation results.

[Explanation of symbols]

A Usage condition extraction means C classification means I input means H Home characteristic analogy K Energy consumption calculation means R storage means S Consumption characteristic analogy means T pretreatment means

   ─────────────────────────────────────────────────── ─── Continued front page    (72) Inventor Minako Nabeshima             1-3-1, Higashi-Awaji, Higashiyodogawa-ku, Osaka City, Osaka Prefecture             -No. 210 (72) Inventor Satoshi Nishiguchi             4-1-2 Hirano-cho, Chuo-ku, Osaka-shi, Osaka Prefecture               Within Osaka Gas Co., Ltd. (72) Inventor Akemi Kubota             4-1-2 Hirano-cho, Chuo-ku, Osaka-shi, Osaka Prefecture               Within Osaka Gas Co., Ltd. F-term (reference) 3L060 AA08 CC02 CC08 CC10 DD08                       EE01

Claims (11)

[Claims] 1. Input means for inputting energy consumption-related measurement data, which is measurement data related to energy consumption of a house, based on predetermined classification conditions for the energy-consumption-related measurement data input to the input means. An energy consumption calculation data creation device for a house, which is provided with a classifying means for classifying, and a storage means for recording the energy consumption related actual measurement data classified by the classifying means for each of the classification conditions. 2. The input means is configured to input a plurality of types of energy consumption-related actual measurement data, and the classifying means outputs each of the plurality of types of energy consumption-related actual measurement data input to the input means. It is configured to perform classification based on a classification condition that is determined in advance corresponding to each type, and the storage unit, for each classification condition, the energy consumption-related actual measurement data of each type classified by the classification unit. The data creation device for calculating energy consumption of a house according to claim 1, which is configured to record. 3. The classification means, among the plurality of types of energy consumption-related measurement data input to the input means, kitchen gas consumption-related measurement data and hot water supply energy consumption-related measurement data, It is configured to classify each of the measured data of power consumption other than for kitchen, hot water supply and air conditioning for each of the classification conditions including at-home characteristics, energy consumption characteristics, family characteristics and day characteristics. 2. The data creation device for calculating the energy consumption of the house described in 2. 4. The classification means includes, among the plurality of types of energy consumption-related actual measurement data input to the input means, air-conditioning energy consumption-related actual measurement data including building characteristics, day characteristics, and area characteristics. The data creation device for calculating energy consumption of a house according to claim 2, wherein the data creation device is configured to perform classification according to each of the classification conditions. 5. A home characteristic analogy means for analogizing the home characteristic is provided, and the home characteristic analogy means is for the kitchen based on the measured data related to gas consumption for kitchen or the measured data related to energy consumption for hot water supply. The energy consumption of the house according to claim 3, which is configured to determine whether or not the energy consumption for hot water supply is consumed, and to infer that at home when there is energy consumption for the kitchen or hot water Calculation data creation device. 6. A consumption characteristic analogy means for analogizing the energy consumption characteristic is provided, and the consumption characteristic analogy means comprises the measured data related to the gas consumption for the kitchen, the measured data related to the energy consumption for hot water supply and the power consumption. The energy consumption calculation for a house according to claim 3 or 5, wherein the energy consumption characteristics of the kitchen gas consumption, the hot water supply energy consumption, and the electric power consumption are analogized based on each of the measured amount data. Data creation device. 7. The input means receives, as the energy consumption-related measurement data, gas consumption measurement data indicating a relationship between a gas consumption amount and time, and the gas consumption measurement data input to the input means is used for a kitchen. Pretreatment means for performing a pretreatment for separating the gas consumption amount is provided, and the classification means uses the gas consumption amount for the kitchen separated by the pretreatment means as the measurement data related to the kitchen gas consumption. It is configured to perform classification based on the classification condition, the pre-processing means, in the gas consumption actual measurement data, the data portion of a gas consumption time zone shorter than a set time is provisionally determined as a kitchen gas consumption amount, and The data portion in the gas consumption time zone longer than the set time is provisionally determined as the provisional determination data portion for heating, and subsequently, the gas consumption amount for the kitchen and the time of the data portion provisionally determined. When corresponding to the kitchen working time, the data portion is determined as the kitchen gas consumption amount, when not corresponding to the kitchen working time, the data portion is determined as the heating gas consumption amount, and, In the data obtained by determining the minimum gas consumption amount in the heating temporary determination data portion from the heating temporary determination data portion, and in the data obtained by subtracting the minimum gas consumption amount from the heating temporary determination data portion, gas consumption shorter than the set time The data portion of the time zone is determined as the gas consumption for the kitchen, and if there is a data portion of the gas consumption time zone that is equal to or longer than the set time, the minimum gas consumption amount in that data portion is determined from the data portion and the minimum gas consumption is determined. The operation of reducing the gas consumption amount and determining the data portion of the obtained data in the gas consumption time zone shorter than the set time as the kitchen gas consumption amount is the total gas consumption time. There energy consumption calculation data generating apparatus according to claim 3, 5 or 6, wherein housing is configured to repeat until shorter than the set time. 8. The hot water supply amount and the hot water supply temperature from the hot water supply device, and the water supply amount of water to be mixed with the hot water from the hot water supply device, as the actual measurement data related to the energy consumption for hot water supply to the input means. And the water supply temperature, the amount of hot water supplied to the input means, the hot water temperature,
Based on the measured data of the water supply amount and the water supply temperature,
The sum of the hot water supply amount and the water supply amount is obtained as the hot water consumption amount, and the product of the hot water supply amount and the hot water supply temperature,
Pretreatment means is provided for performing a pretreatment for obtaining the sum of the product of the water supply amount and the water supply temperature as the heat quantity of hot water, and the classification means is the hot water consumption amount obtained by the pretreatment means. 7. The data generating device for calculating energy consumption of a house according to claim 3, 5 or 6, which is configured to classify the hot water heat quantity for each of the classification conditions. 9. A use condition extracting means for obtaining an air conditioner use time zone and a set room temperature from measured data relating to the energy consumption for air conditioner input to the input means, and an air conditioner use time obtained by the use condition extracting means. Based on the band and the set room temperature, the energy consumption calculation means for calculating the energy consumption for air conditioning by a predetermined heat load calculation program is provided, and the classification means, the energy for air conditioning obtained by the energy consumption calculation means. The data generation device for calculating energy consumption of a house according to claim 4, wherein the consumption amount is classified based on each of the classification conditions. 10. A computer for creating data for calculating energy consumption of a house, input means for inputting energy consumption-related measurement data which is measurement data related to energy consumption of a residence, and said input means inputted to the input means. A classification unit that classifies the energy consumption-related actual measurement data based on a predetermined classification condition and a storage unit that records the energy consumption-related actual measurement data classified by the classification unit for each of the classification conditions Data creation program for home energy consumption calculation for. 11. An input means for inputting energy consumption measurement data, which is measurement data relating to energy consumption of a house, to a computer for creating data for calculating energy consumption of a residence, the input means inputting the input data. A classification unit that classifies the energy consumption-related actual measurement data based on a predetermined classification condition and a storage unit that records the energy consumption-related actual measurement data classified by the classification unit for each of the classification conditions A computer-readable recording medium in which a program for creating data for calculating energy consumption of a house is recorded.