JP2005130581A - Power loading pattern predicting apparatus - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a power loading pattern predicting apparatus which easily predicts a power loading pattern in a building in a high accuracy. <P>SOLUTION: To a data controller 4 provided in the power loading pattern predicting apparatus 2, an input unit 6 for inputting an amount of power consumption and the like in months when air conditioning is not used in a target building for prediction, a data storage unit 8 for storing the inputted amount of the power consumption in the months when the air conditioning is not used and living information, such as the number of home persons in a daytime in the target building for prediction, and a load pattern data storage unit 10 for storing the load pattern showing a changing state of the power consumption according to a time, are connected. To the data controller 4, a display unit 12 for displaying the power loading pattern formed by using the predicted value of the power consumption by calculating the predicted value of the power consumption according to the hours in the target building for prediction by using the lowest power calculated based on the power consumption according to the unused date of the air conditioning and the selected load pattern, is connected. <P>COPYRIGHT: (C)2005,JPO&NCIPI

Description

The present invention relates to a power load pattern prediction apparatus that predicts a power load pattern in an arbitrary building.

Conventionally, the power load pattern for each time in a building has been predicted using the operation schedule for each time of the equipment and the power consumption when each equipment is used. The operation schedule of equipment is based on how the consumer uses which equipment in which room at what time.
Since it is necessary to set the schedule one by one, it is necessary to grasp the entire life schedule of the consumer, and the operation schedule of the equipment is created based on the grasped life schedule of the consumer. In order to simplify the work of creating this operation schedule, for example, at a symposium of the Air Conditioning and Sanitation Engineering Society, people in buildings based on questionnaire results, working men, working women, family women, high school students, junior high school students, It was proposed to classify and use eight types of standard life schedules, classifying them into elementary school students, men over 70 years old, and women over 70 years old. That is, it has been proposed to classify consumers into groups and create a driving schedule using a life schedule set for each group.

Here, the process in the case of estimating the electric power load pattern according to time in a building using 8 types of standard life schedules is demonstrated. First, the shape of the building to be predicted, floor plan, equipment, fittings, geographical conditions, weather conditions, seasons, contents of external forces that cause fluctuations in the physical load and power load pattern, and formulas used for analysis And input physical laws. next,
The residents living in the building are classified into any of the above-described eight types of consumers, and each standard living schedule is selected. Next, the operation schedule of the equipment is determined from the individual life schedule of the consumer. Then, confirm whether there is any interference from other consumers about the life schedule of all the consumers, correct the life schedule and the operation schedule of the equipment, and set each equipment equipment set in advance. The power load pattern according to the time was predicted using the power consumption.

In addition, there exists an input data creation device for environmental analysis simulation that accurately creates a life schedule of a consumer and an operation schedule of equipment used when predicting a power load pattern in a building (see, for example, Patent Document 1). . According to this environment analysis simulation input data creation device, personal information such as the gender, occupation, age, and priority of device use for each room is input, and the database of the life schedule is based on the input personal information. Select life schedule data that suits the consumer from among them. And according to the selected life schedule, the operation schedule of equipment is automatically set.

In addition, as a method for easily predicting a power load pattern, for example, a power load pattern serving as some models such as large, medium, and small is set, and is set based on a result of a questionnaire or the like for consumers. There is a method for selecting the closest power load pattern from among the power load patterns.

Japanese Patent Laid-Open No. 10-326294

By the way, when predicting the power load pattern by time of the building using the operation schedule by time of the equipment and the power consumption when using each equipment, the development and design of the system because the prediction accuracy is high. It is effective as a detailed simulation when performing the above.
However, as described above, there is a large amount of information necessary for predicting the power load pattern for each time in the building, and there is a problem that it takes time to input such information. For this reason, for example, when a salesperson predicts a power load pattern according to time in a building at the time of customer service, it is difficult to quickly simulate a power load pattern, and as a sales support tool, etc. It was difficult to use. On the other hand, when predicting a power load pattern according to time in a building using a simple method, since various power load patterns are originally applied to a limited pattern, the accuracy of the prediction is significantly reduced. There was a problem.

The subject of this invention is providing the electric power load pattern prediction apparatus which estimates the electric power load pattern according to time in a building easily and with high precision using few information.

The power load pattern predicting device according to claim 1 is configured such that a power consumption storage unit that stores a power consumption amount of an air conditioning unused month in a prediction target building, and a power consumption amount stored in the power consumption storage unit. Based on the minimum power calculation means for calculating the minimum power in the building to be predicted, the load pattern storage means for storing the load pattern indicating the fluctuation state of the power consumption by time, and the load pattern storage means. Load pattern selection means for selecting a predetermined load pattern from among the load patterns, the minimum power calculated by the minimum power calculation means and the predetermined load pattern selected by the load pattern selection means, A power consumption calculating means for calculating a predicted value of power consumption at each time of day in the prediction target building, and the power consumption calculating means Based on the issued predicted value of the power consumption, characterized in that it comprises a power load pattern generating means for generating a power load pattern in the prediction target building.

According to the power load pattern predicting apparatus of the first aspect, the 1 in the prediction target building is calculated using the minimum power calculated based on the power consumption in the air conditioning unused month in the prediction target building.
A predicted value of power consumption at each time of day is calculated, and a power load pattern is created based on the calculated predicted value of power consumption. Therefore, the power load pattern in the prediction target building can be created without performing a process such as setting an operation schedule of the equipment installed in the prediction target building. Therefore, it is possible to easily and easily predict the power load pattern in the prediction target building.

The power load pattern prediction device according to claim 2, wherein the load pattern selection unit includes a life information input unit that inputs a living information of a consumer in the prediction target building, and is input by the living information input unit. The load pattern is selected based on the life information.

According to the power load pattern predicting apparatus according to claim 2, a load pattern is selected based on life information of a consumer in the prediction target building, and the power load pattern in the prediction target building is selected using the selected load pattern. Have created. Therefore, it is possible to easily predict an appropriate power load pattern that reflects the lives of consumers in the building to be predicted with high accuracy.

Further, in the power load pattern prediction device according to claim 3, the life information includes at least one of the number of people staying at home in the prediction target building and the return time of the consumer,
When the life information includes the number of people staying at home during the day, the load pattern selection means selects a load pattern corresponding to the number of people staying at home during the day, and the home information is returned to the life information. When the time is included, a load pattern corresponding to the return time is selected.

According to the power load pattern predicting apparatus of the third aspect, the load pattern is selected based on the number of people staying at home during the day or the return time of the consumer, and the power load pattern in the prediction target building is created. Therefore, it is possible to easily create a high-accuracy power load pattern that appropriately reflects the living conditions of consumers in the building to be predicted, for example, accurately predicting the peak of power consumption.

According to this invention, the power load pattern in the prediction target building is created on the basis of the load pattern indicating the power consumption amount of the air conditioning unused month in the prediction target building and the fluctuation state of the power consumption by time. Therefore, various information is input to predict the power load pattern, the living state of each consumer in the prediction target building is grasped, and the operation schedule of the equipment installed in the prediction target building is determined. The power load pattern can be predicted quickly and easily without performing any complicated processing. Therefore, for example, a sales person can quickly present a power load pattern to a customer, and can be used as a useful sales support tool.

Hereinafter, a power load pattern prediction apparatus according to an embodiment of the present invention will be described with reference to the drawings. FIG. 1 is a block configuration diagram of a power load pattern prediction apparatus according to an embodiment. As illustrated in FIG. 1, the power load pattern prediction device 2 includes a data control unit 4 that controls data processing in the power load pattern prediction device 2. The data control unit 4 includes an input unit 6 for inputting the power consumption amount of the air conditioning unused month used when calculating the power consumption in the prediction target building, and the air conditioning unused input through the input unit 6. A data storage unit 8 that stores the amount of power consumption of the month and the like is connected. Also connected to the data control unit 4 are a load pattern data storage unit 10 that stores load pattern data indicating power consumption fluctuation status according to time and a display unit 12 that displays a power load pattern and the like in the prediction target building. Has been.

The data storage unit 8 stores the power consumption amount of the prediction target building input via the input unit 6 in the air conditioning unused month, that is, the power consumption amount of the month in which no cooling or heating is used. The data storage unit 8 stores information about the life of a person (resident) living in the prediction target building as life information input via the input unit 6. This life information includes, for example, information such as the area where the prediction target building exists, the season for predicting the power load pattern, the number of people staying at home during the day, and the like. In addition, instead of the information on the number of people staying at home during the day, or in addition to the information on the number of people staying at home during the day, information such as the time when a person who is out during the day returns home may be included.

Further, the load pattern data storage unit 10 stores a load pattern of power consumption indicating a fluctuation state of power consumption by time. The load pattern of power consumption is stored for each combination of the region, season, and life information in consideration of the region, season, and life information.

For example, as an example of a load pattern in the case of combining any region, any season, and any living information, as shown in FIG. 2, the load pattern for each number of people in the day in any season in any region It is remembered. As a load pattern for each person at home during the day, FIG.
As shown in the figure, when the number of people at home during the day is 0, in the case of 1 and in the case of 2 or more, a numerical value indicating the rate of increase in load for each number of people at home is stored every hour. Has been.

In addition, as another example of the load pattern in the case of combining any region, any season, and any living information, as shown in FIG.
A load pattern is stored for each return time of the returnee. As shown in FIG. 3, the load pattern for each return home time of the returnee is as follows. When all the residents are at home, when the return home time is 17:00 to 19:00, 19:00 to 21:00 In the case of 21:00 to 23:00, in the case of 23:00 or later, a numerical value indicating the load increase rate at each return time is stored every hour.

The regions are, for example, Hokkaido, Tohoku, Hokuriku, Kanto, Shin-Etsu, Chubu, Kansai, China, Shikoku, Kyushu, etc., and the seasons are, for example, spring, summer, autumn, and winter. Thus, for example, the load patterns corresponding to the combination of “Hokkaido”, “Spring”, and “Number of people at home during the day”, “Hokkaido” and “
Load pattern corresponding to the combination of “spring” and “return time of returnee”, “Hokkaido” and “summer”
And the load pattern corresponding to the combination of “the number of people staying in the daytime”, the load pattern corresponding to the combination of “Hokkaido” and “summer” and “the return time of the returnee”, etc. The load pattern corresponding to the combination of the number of people staying at home and the daytime, and the load pattern corresponding to the combination of the season of each region and the return time of the returnee are stored in the load pattern data storage unit 10.

Next, a power load pattern prediction process in the power load pattern prediction apparatus according to the embodiment of the present invention will be described with reference to the flowchart of FIG.

First, the power consumption of the air conditioning unused month in the building to be predicted is input via the input unit 6 (step S10). Here, for example, in the case of Tokyo, the amount of power consumed in the month when the air conditioning is not used is a month such as April, May, or October. Therefore, in the following, a case where the power consumption for October is input will be described as an example. Note that this power consumption is described in, for example, “Notice of Usage” sent from the power company, so refer to “Notice of Usage” etc.
The power consumption for 0 month (for example, 300 kWh) is input via the input unit 6. Further, the input power consumption amount for October is stored in the data storage unit 8.

Next, life information of a person living in the prediction target building is input via the input unit 6 (step S11). For example, life information including information such as the area where the prediction target building exists, the season in which prediction is performed, and the number of people who are present at the prediction target building during the day is input via the input unit 6. The input life information is stored in the data storage unit 8.

Next, the power consumption amount of the air conditioning unused month input in step S10 and step S1
Based on the life information input in 1, the minimum power in the prediction target building is calculated (step S12). Here, the lowest power is the lowest power in the load pattern according to the time of day, and is, for example, the power consumed only by a device that is always connected to an outlet such as a refrigerator. The minimum power is calculated using the power consumption amount of the air conditioning unused month stored in the data storage unit 8, life information, and the load pattern stored in the load pattern data storage unit 10. In the following, specific processing for calculating the minimum power will be described.

First, the power consumption amount in the month when the air conditioning is not used, that is, the power consumption amount in October is divided by the number of days in October “31 days” to calculate the power consumption amount per day (daily average power consumption amount). Next, based on life information, the area where the prediction target building exists, the number of people at home in the prediction target building during the day,
A load pattern corresponding to October (Autumn) is extracted from the load patterns stored in the load pattern data storage unit 10. Next, based on the daily average power consumption and the extracted load pattern, a graph showing the power load pattern of any one day in October in the prediction target building is created (see FIG. 5). Here, since the lowest power is the lowest power among the power consumption shown by the power load pattern of FIG. 5, it shows the power load pattern of any one day in October in the prediction target building shown in FIG. The lowest value in the graph is the lowest power value. The calculated minimum power value is stored in the data storage unit 8.

Next, a minimum power amount is calculated based on the minimum power calculated in step S12 (step S13). That is, the minimum power amount is calculated using the function “minimum power amount = minimum power × 24 hours × days” stored in the load pattern data storage unit 10. Here, as the number of days, since the power consumption amount in the non-air-conditioning month used when calculating the minimum power is the power consumption amount in October, 31 days, which is the number of days in October, is used. The calculated minimum electric energy is 1
This corresponds to one month of power consumption when only the lowest power is used every day for a month. Further, the calculated minimum power amount is stored in the data storage unit 8.

Next, the load pattern of the power consumption which shows the fluctuation state of the power consumption used when predicting the power load pattern in the prediction target building is selected from the load patterns stored in the load pattern data storage unit 10 ( Step S14). For example, when creating a February power load pattern for a prediction target building that exists in Tokyo and has one person in the daytime, the number of people in the daytime in the Kanto region in winter is one. A corresponding load pattern is selected.

Next, a predicted value of power consumption by time in the prediction target building is calculated (step S15).
). First, the daily average of the minimum amount of power, that is, the amount of power divided by the number of days (31 days) (
Calculate the daily average minimum power). And the calculated daily average minimum electric energy, step S12
Based on the daily average power consumption calculated in step 1 and the selected load pattern, “power consumption at each time = (daily average power consumption−daily average minimum power amount) × ratio of load pattern at each time + minimum power” ”To calculate a predicted value of power consumption at each time. Note that the calculated predicted value of power consumption at each time is stored in the data storage unit 8.

When predicted power consumption values at all times of the day are calculated (step S16)
Then, a power load pattern is created based on the calculated predicted value of power consumption (step S17).
). That is, based on the predicted value of power consumption at each time of 24 hours, a graph showing the daily power load pattern in the prediction target building is created. The created graph indicating the power load pattern is stored in the data storage unit 8.

Next, a graph showing the generated power load pattern is displayed (step S18). For example, as shown in FIG. 5 described above, a graph indicating the power load pattern of any one day in any season in the prediction target building is displayed on the display unit 12.

According to the power load pattern prediction apparatus according to the embodiment of the present invention, the prediction target is based on the input power consumption and life information of the unused air conditioning month and the predetermined load pattern selected based on the life information. A predicted value of power consumption at each time in the building is calculated to create a power load pattern. Therefore, the power load pattern in the prediction target building can be easily created, and a highly accurate power load pattern can be created based on a small amount of input information.

In addition, according to the power load pattern prediction apparatus according to the embodiment of the present invention, a highly accurate power load pattern is created only by inputting less information than in the conventional method. Therefore, sales representatives who are serving customers can easily and quickly create power load patterns in front of customers, and appropriately conduct sales activities when newly installing equipment. Can help. For example, for a customer who is considering replacement of hot water supply equipment, a power load pattern is created using the house where the customer lives as a prediction target building, and the old and new calculated based on the created power load pattern The utility costs can be compared when using hot water supply equipment.

In addition, in the above-mentioned embodiment, although the electric power load pattern in the prediction object building is created, you may make it utilize the produced electric power load pattern for control of the equipment in the prediction object building. For example, in a cogeneration system using a fuel cell, it is desirable to operate at a time when the power consumption is higher. Therefore, based on the created power load pattern, the system operation is stopped at the time when the minimum power is reached. In addition, control for efficiently operating the equipment may be performed, such as starting operation at a time when power consumption increases.

Further, in the power load pattern prediction apparatus according to the above-described embodiment, the living information includes the number of people at home during the day or the time of returning home, but other information may also be included. For example, detailed information related to life such as wake-up time, time to leave for a company or school, and bedtime may be included. In addition, when detailed information about life is included, a more appropriate load pattern may be selected using the detailed information.

Further, in the power load pattern prediction device according to the above-described embodiment, as a load pattern of power consumption, a load pattern corresponding to a combination of the area, the season, and the number of people at home during the day,
Although the load pattern corresponding to the combination of the region, the season, and the return time of the returnee is stored, only one of the load patterns may be stored. That is, only the load pattern corresponding to the combination of the area, the season, and the number of people staying in the day, or only the load pattern corresponding to the combination of the area, the season, and the return time of the returnee may be stored.

In addition, the power load pattern prediction device according to the above-described embodiment does not include a communication control unit, but includes a communication control unit and is connected via a communication control unit to a portable information terminal provided by a salesperson or the like. May be. In other words, a sales representative or the like
Using portable information devices such as Assistants and notebook personal computers,
The load pattern data indicating the fluctuation state of the power consumption stored in the load pattern data storage unit 10 included in the power load pattern prediction device 2 may be extracted so that the power load pattern in the prediction target building can be created. .

It is a block block diagram of the electric power load pattern prediction apparatus which concerns on embodiment of this invention. It is a figure which shows an example of the load pattern for every number of people at home during the day which concerns on embodiment of this invention. It is a figure which shows an example of the load pattern for every return time of the consumer (returning person) which concerns on embodiment of this invention. It is a flowchart for demonstrating the process which estimates the electric power load pattern which concerns on embodiment of this invention. It is a figure for demonstrating the process at the time of calculating the minimum electric power which concerns on embodiment of this invention.

Explanation of symbols

2 ... Power load pattern prediction device, 4 ... Data control unit, 6 ... Input unit, 8 ... Data storage unit,
10: Load pattern data storage unit, 12: Display unit.

Claims (3)

Power consumption storage means for storing the power consumption of the air conditioning unused month in the building to be predicted;
Based on the power consumption stored in the power consumption storage means, the minimum power calculation means for calculating the minimum power in the prediction target building;
Load pattern storage means for storing a load pattern indicating a fluctuation state of power consumption by time;
Load pattern selection means for selecting a predetermined load pattern from among the load patterns stored in the load pattern storage means;
Consumption for calculating a predicted value of power consumption at each time of day in the prediction target building based on the minimum power calculated by the minimum power calculation means and the predetermined load pattern selected by the load pattern selection means Power calculation means;
A power load pattern prediction device comprising: a power load pattern creation unit that creates a power load pattern in the prediction target building based on a predicted value of power consumption calculated by the power consumption calculation unit. The load pattern selection means includes
Living information input means for inputting life information of a consumer in the prediction target building,
2. The power load pattern prediction apparatus according to claim 1, wherein the load pattern is selected based on life information input by the life information input means. The life information is
Including at least one of the number of people staying in the day in the building to be predicted and the return time of the consumer,
The load pattern selection means includes
When the number of people staying at home during the day is included in the life information, a load pattern corresponding to the number of people staying at home during the day is selected, and the time when the return home of the consumer is included in the life information The power load pattern prediction device according to claim 2, wherein a load pattern corresponding to the time of returning home is selected.

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