JPH08322147A - Demand prediction system - Google Patents

Abstract

PURPOSE: To obtain a demand prediction system in which the prediction accuracy can be enhanced while taking account of power consumption of an air conditioner accurately. CONSTITUTION: A model configuration section 6 determines a power consumption model and an indoor temperature predicting section 7 predicts the indoor temperature based on the power consumption model. A demand analysis section 8 determines the relation of the achievement of demand, the predicted indoor temperature and the achievement of outdoor temperature. Future indoor temperature and outdoor temperature are then substituted for the relation determined at the demand analysis section 8 thus predicting the demand. Results of prediction and analysis are presented at a display section 3.

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a power demand forecasting system for forecasting future power consumption.

[0002]

2. Description of the Related Art Electric power is generally not stored, but simultaneously produces and consumes electricity. Therefore, it is necessary to prepare a power generation plan so that power outages will not occur due to insufficient power supply. Therefore, it is necessary to accurately predict future power demand in the sector that supplies power.

The daily maximum power demand changes depending on the day of the week, the season, and the like. For example, since companies and the like are closed on Sundays, the power demand is lower than on weekdays (Monday to Friday). The demand for air conditioning in summer and the demand for heating in winter will increase demand compared to spring and autumn. The fluctuation of electricity demand on weekdays in summer is most affected by the highest temperature of the day.
This is because the higher the maximum temperature, the greater the number of air conditioners (air conditioners) that can be used for cooling, and the more power required for cooling. Accurately predicting this fluctuation due to the air conditioner will improve the accuracy of forecasting overall demand. A weather forecast source that provides meteorological information, such as the Meteorological Agency, forecasts the maximum temperature and the minimum temperature for the next week on the next day. When the minimum temperature on the day is high, the demand for electricity also tends to increase. This is because there is a correlation between the highest temperature and the lowest temperature in the first place. For example, on days when the minimum temperature is high, the maximum temperature tends to be high. The reason why not only the maximum temperature but also the minimum temperature is used is to increase the variables that affect the power demand as much as possible to improve the prediction accuracy of the power demand.

Conventionally, there is one that uses the maximum temperature and the minimum temperature to predict the maximum power demand (Japanese Patent Laid-Open No. 3-2.
12702). In this method, a multiple regression process is performed using a model formula to obtain a predicted value. In this model formula, the maximum temperature, the minimum temperature, the humidity, the weather (cloudy weather, rain, etc.) and the like are used as factors that affect the predicted value.

[0005]

As described above, there is no doubt that the maximum temperature and the minimum temperature are the factors that affect the maximum demand for electric power. However, in reality,
Even if the maximum and minimum temperatures are included in the forecast factors, the forecast values and the actual maximum demand may not match. That is, there is a problem that the prediction accuracy cannot be improved. According to the study by the inventors, one of the causes is considered to be the use of the air conditioner. Therefore, this will be described.

[0006] For example, in the case of summer, the power consumption by the air conditioner depends on the number of the air conditioners that are switched on and the power required to cool the indoor air temperature to keep it constant regardless of the outdoor air temperature. Decided. It can be seen that in both cases, if the maximum temperature rises, the number of units and power consumption will increase. However, even though the maximum temperature is the same, there are many days when the power demand is different. This is because the factor affecting the operation of the air conditioner is not the outdoor temperature but the indoor temperature in the morning in the room with the air conditioner. As the indoor temperature rises, the number of switch-on air conditioners increases. As the number of units increases, even if the maximum temperature is the same, the rise in the demand for electricity in the morning increases and the demand for electricity also tends to increase. On the other hand, at the time of occurrence of the maximum temperature, generally, many air conditioners have already been switched on, so that the room temperature is kept constant. However, when it is late at night and the air conditioner is switched off, the indoor temperature rises with time due to the temperature difference between the indoor temperature and the outdoor temperature until then. And usually,
Since a heat source, for example, a refrigerator, an electric device such as a lighting fixture, a human body, and the like exist inside the room, the indoor temperature is higher than the outdoor temperature. Therefore, the indoor temperature and the outdoor temperature in the morning are different. As described above, the fluctuation factor of the electric power demand depends on the electric power consumption by the air conditioner, but even if the maximum temperature and the minimum temperature are the same, the electric power demand may be different, so that the electric power demand prediction accuracy is deteriorated.

The object of the present invention is based on the above situation.
It is to provide a power demand forecasting system with good forecasting accuracy.

[0008]

In order to achieve the above object, the present invention uses the indoor temperature together with the maximum temperature,
It is designed to predict power demand.

According to the first aspect of the present invention, an indoor air temperature predicting unit for predicting an indoor air temperature of a building by a heat model defined using at least the input / output characteristics of the heat of the building and the outdoor air temperature. A demand analysis unit that obtains a causal relationship between the indoor temperature predicted by the indoor temperature prediction unit and the power demand, and a demand prediction unit that predicts the power demand using the indoor temperature based on the obtained causal relationship. An electric power demand forecasting system is provided.

The demand analysis section can perform a regression analysis using, for example, the indoor temperature and the maximum temperature to obtain a causal relationship. In addition, the demand analysis unit,
Perform regression analysis using maximum and minimum temperatures,
It is also possible to adopt a configuration for obtaining a causal relationship. Further, the demand analysis unit may be configured to perform a regression analysis using the indoor temperature, the maximum temperature, the minimum temperature, and the humidity to obtain a causal relationship.

The indoor air temperature predicting unit may be configured to predict the indoor air temperature of the building by using the heat input / output characteristics of the building, the outdoor air temperature, and the daily dose. Further, the indoor air temperature predicting unit may be configured to acquire the outdoor air temperature and the solar radiation amount data from the expected weather data and predict the indoor air temperature.

The demand analysis unit and the demand prediction unit may be configured to perform a regression analysis using the electric power demand and the indoor temperature to obtain a causal relationship.

The display unit may be configured to generate a display screen for displaying the predicted power demand. Also,
The display unit can display a temporal change in the indoor air temperature predicted by the indoor air temperature predicting unit and a temporal change in the outdoor air temperature. The display unit may be configured to display the indoor temperature predicted by the indoor temperature prediction unit and the demand predicted by the demand prediction unit. Furthermore, the display unit may be configured to display a prediction error that is a difference between the indoor temperature, the actual power demand, and the predicted value.

According to a second aspect of the present invention, in addition to the first aspect, an indoor temperature collecting unit for collecting temperature data measured by an air temperature sensor arranged in a building is further provided and measured. There is provided a power demand forecasting system characterized by performing demand analysis and demand forecasting by using temperature data as indoor temperature. As the temperature sensor, for example, a temperature sensor of an air conditioner can be used.

According to a third aspect of the present invention, in addition to the first aspect, a forecast weather collecting section for gathering forecast weather data from a plurality of weather forecast sources and forecasts forecast by each weather forecast source. Provided is a power demand forecasting system characterized by forecasting power demand, further comprising: a forecast weather determination unit that calculates a difference between weather and actual weather and selects a weather forecast source having the smallest error. It

[0016]

The indoor air temperature predicting unit predicts the indoor air temperature of the building by a heat model defined by using at least the heat input / output characteristics of the building and the outdoor air temperature. That is, for example, by using a building model, the indoor temperature is predicted by considering the input heat due to the difference between the outdoor temperature and the indoor temperature and the input heat due to solar radiation. The demand analysis unit obtains a causal relationship between the indoor temperature predicted by the indoor temperature prediction unit and the power demand. This causal relationship can be obtained by regression analysis, for example. The demand predicting unit predicts the power demand using the indoor temperature based on the obtained causal relationship. In this way, by adding the room temperature to the factor for prediction, it is possible to predict the power demand including the effect of switching the air conditioner on and off.

[0017]

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS A first embodiment of the present invention will now be described with reference to FIGS.
4 will be described.

FIG. 1 is a diagram showing the configuration of the power demand forecasting system of the present invention. The power demand prediction system shown in FIG. 1 includes an input unit 1, a control unit 2, a display unit 3, and a database 1.
1, switching unit 4, database editing unit 5, model building unit 6, indoor temperature prediction unit 7, demand analysis unit 8, demand prediction unit 9,
It has an evaluation unit 10 and an indoor air temperature collection unit 19.

The database 11 is specifically the equipment database 12, the power consumption model database 13, and the meteorological achievement database 1 according to the contents of the accumulated data.
4. It has a forecast weather database 15, an indoor temperature forecast database 16, a demand forecast model database 17, and a power demand record database 18. The database 11 is composed of, for example, a mass storage device such as a magnetic disk device or an optical disk device. More specifically, the plurality of types of databases described above are constructed in one or more magnetic disk storage devices. For example, one type of database may be used for each magnetic disk device. Further, the database 11 may be configured by a disk array system including a plurality of magnetic disk devices, optical disk devices and the like. In the example shown in FIG. 1, in actuality, even if there are a plurality of magnetic disk devices, a control for controlling writing and reading of the storage medium is performed so that the storage device functions like an apparent one storage device. It contains a device (not shown).

The input section 1 and the control section 2 are connected by a control line D11. Here, the connection means that a signal can be transmitted or received between the processing units. The input unit 1 includes, for example, a keyboard 1a,
It is composed of various input devices such as a mouse 1b and a touch panel. The input unit 1 receives the input operation of the operator and transmits the input data to the control 2 via the control line D11. There are two types of data input by the input unit 1. One of them is whether the demand forecast, which is a forecast condition, is to be forecast for the whole area or for each area, and the setting data indicating whether the indoor temperature to be used is the predicted value or the actual value. The other is the setting of the operation mode. In the latter example, there is an input for selecting a processing condition from the information displayed on the display unit 3.

The control unit 2 is connected to the input unit 1 via a control line D31, is connected to the display unit 3 via a bus D2 composed of a control line D21 and a data bus D22, and is connected to the control line D3.
It is connected to the switching unit 4 by a bus D3 including 1 and a data bus D32. The control unit 2 processes the input from the input unit 1, selects a mode of operating any of the processing units 5-10 and 19 described later, and processes each of the processing units 5-10 and 1
Input / output processing 9 and display control of the display unit 3 are performed. For example, the control unit 2 takes in display screen data such as a data processing result via the data bus D32 and outputs the data to the display unit 3. Further, the control unit 2 clears the display screen by outputting a control signal for clearing the display screen through the control line D21 before transmitting the display data to the display unit 3 through the data bus D22. Further, the control unit 2 also functions as a mode selection unit, and outputs a signal instructing which of the processing units 5-10 and 19 is operated via the control line D31 according to the operation mode input from the input unit 1. It transmits to the switching unit 4. Further, when the processing condition is selected by the input unit 1 from the information displayed on the display unit 3, the control unit 2 transmits the selected signal to the switching unit 4 through the control lines D11 and D31, and the processing unit 5 -10 or 19 is received by any of the corresponding processing units. Thereby, the corresponding processing units 5-10,
Start any one of 19.

The control unit 2 is composed of, for example, an information processing device having at least a central processing unit and a memory.

The display unit 3 is composed of a display device such as a CRT display device or a liquid crystal display device, and displays information sent from the control unit 2.

The switching unit 4 contains a switch circuit 4a for selectively connecting the processing units 5-10 and 19 and a control circuit 4b for controlling the switch circuit 4a. The switching unit 4 is connected to the control unit 2 and the database 11. The switching unit 4 is connected to or disconnected from the processing units 5-10 and 19 via the switch circuit 4a. The switch circuit 4a receives the operation mode signal input from the input unit 1 and the switching unit 4 itself and the processing unit 5-1.
The processing unit of either 0 or 19 is connected.

For example, when a mode for operating the database editing unit 5 is input from the input unit 1, the database editing unit 5 sets the control line S51 (data bus S52), the switching unit 4 and the control line D51 (data bus D52). It is connected to the database 11 via the control line S51 (data bus S52), the switching unit 4 and the control line D31 (data bus D32). When the data in the database 11 is required in the process of the database editing unit 5, a request signal for the required data is transmitted to the database 11 through the control line D51 (S51).
Upon receiving this request signal, the database 11 transmits the corresponding data to the database editing unit 5 through the data bus D52 (S52). At this time, the control line D5
Through 1 (S51), the database 11 transmits a signal that data has been transmitted to the database editing unit 5, and the database editing unit 5 transmits to the database 11 that the data has been received. Even after a certain period of time has passed, the database editing unit 5 changes the database 1
When the signal that the data is received does not come to 1, the database 11 retransmits the data to the database editing unit 5. When data is stored in the database 11 by the processing of the database editing unit 5, the control line D51 is used.
A signal designating a data storage location is transmitted to the database 11 through (S51), and the data bus D52 (S5) is transmitted.
When data is sent to the database 11 through 2), this data is stored in the storage location of the database 11 designated by the control line D51 (S51). When the data is stored, the database 11 sends the stored information to the database editing unit 5. When the request signal is received, the corresponding data is transmitted to the database editing unit 5. At this time, through the control line D51 (S51), the database 1
From No. 1, a signal that data has been transmitted is transmitted to the database editing unit 5, and when the database editing unit 5 receives the data, it transmits the reception to the database 11. If no signal is received from the database 11 to the database editing unit 5 after a certain period of time, the database editing unit 5 determines that the database 1
Retransmit the data to 1. The connection relationship between the processing units and the data flow when the database editing unit 5 is selected as the operation mode from the input unit 1 have been described above. However, the operation mode of the processing units 6-10 and 19 is different from that of the input unit 1. Even when input, the database 11 and the processing units 6-10 and 19 similarly transmit and receive data and control signals, and transmit and receive data.

As described above, the processing units 5-10 and 19 include the database editing unit 5, the model building unit 6, the indoor temperature prediction unit 7, the demand analysis unit 8, the demand prediction unit 9, the evaluation unit 10, and the indoor temperature collection. It is composed of the section 19. Each processing unit is composed of an information processing device that operates independently. That is, although not shown, each has a central processing unit, a memory, etc., and performs information processing based on a program given in advance. 21-k arranged outside the system are connected to the indoor air temperature collecting unit 19 via communication lines C11 ... Cnk. Room temperature Ti measured by these data measuring units 21-1, ... 21-k
n, outdoor temperature Tout, and other data are collected.

The processing units 5-10 and 19 can be configured by a common information processing device for some or all of them. For example, the processing units 5-10 and 19 may be configured by one information processing device. In this case, the control unit 2 described above may also be configured by the information processing device.

Next, the operation of the power demand forecasting system will be described. In this system, there are a process of setting a prediction condition and a process in any one of the seven operation modes in which any one of the processing units 5-10 and 19 is operated. First, after describing the setting of the prediction condition, the processing in each operation mode will be described based on two types of examples according to the indoor temperature to be used.

Selection of a prediction condition and an operation mode will be described with reference to FIG. The control unit 2 displays the prediction condition setting area 31 on the display screen 300 of the display device 3 as shown in FIG.
A screen including 0 and the operation mode setting area 315 is displayed. In the prediction condition setting area 310, the area 311 and the operating temperature 312 are selected as prediction conditions. In the area 311, if the demand composition and the weather of the area of the demand forecast are uniform, the whole area may be selected, and if the area is not uniform, the area is selected. If you select the former, the demand is forecasted in a lump. The operating temperature 312 is classified into two, that is, a prediction 312a and an actual result 312b, depending on the indoor temperature used. Which of the prediction 312a and the actual result 312b is used depends on whether or not the actual temperature can be observed.
When an area is selected in the area 311, the operating temperature can be selected for each area. These selections can be made by checking the square of the screen 310 with the mouse 1b or the like. When any one of the squares is selected, the control unit 2 displays "X" in the square. The selected display is
It is not limited to this. For example, the display mode may be changed such as changing the color or changing the brightness.

The operation mode can be set by checking the square in front of the mode 1-7 displayed as a menu in the operation mode setting area 315 with the mouse 1b or the like. The setting by this check becomes the setting of the start command of the mode, and the processing of the selected mode is automatically started.

In the item of demand 313, when the demand forecast ends, the forecast result is displayed. For regions, the sum of each region is displayed. If you check "END" 316,
The data on the screen is cleared.

The demand forecasting process is the same regardless of whether the area is the whole area or the area. The difference between whether the area is the whole area or the area is
The only difference is that the databases to be accessed are global or regional. Therefore, in the following description, it is assumed that the entire area is selected, and if the indoor temperature is initially predicted to be 312a, the next is the actual result 3
12b.

First, the case where the indoor temperature is predicted will be described. Here, the processing of each processing unit will be described according to each mode.

First, the model construction mode is shown in FIG.
From FIG. 19 onward will be described. The operation of selecting the model construction mode will be described with reference to FIGS. 1 and 2, and the processing of the model construction unit 6 will be described with reference to FIGS. 3 to 19. In order to enter the model construction mode, the menu “1. Edit database” in the operation mode setting area 315 displayed on the display unit 3,
From "2. Model construction", "3. Indoor temperature prediction", "4. Demand analysis", "5. Demand prediction", "6. Evaluation", from the input unit 1 with the mouse 1b, before "2" The model construction mode is selected by checking the squares or by inputting the number "2" with the keyboard 1a. This mode signal is transmitted to the control unit 2 through the control line D11. The control unit 2 determines whether the mode input from the input unit 1 matches the model building mode, and when determining that they match, the switching unit 4 selectively switches the switch circuit 4a and the model building unit 6 side. By instructing the connection, the model building unit 6 and the database 11 are connected. This completes the connection for operating the model building unit 6.
This connection relationship continues until the process of the model building unit 6 ends and the model building unit 6 sends a processing end signal to the control unit 2 via the control lines S61 and D31. When the control unit 2 receives the processing end signal from the model construction unit 6, the control line D3
The switch is turned off by 1 to disconnect the control unit 2 and the model building unit 6. At this time, the control unit 2 controls the processing unit 5-10.
Is not connected to any of the. As a result, the input unit 1
Unless a new operation mode is input from the processing unit 5-1
0 never works.

Next, the process of the model building unit 6 and the output screen of the display unit 3 accompanying this process will be described. Figure 3,
FIG. 4 shows a processing flow of the model construction unit 6. The model construction unit 6 creates a heat input / output model in consideration of heat input from the outside and heat discharge from the air conditioner due to the difference in temperature inside and outside the building. When the control unit 2 and the model building unit 5 are connected by the switch circuit 4a, the model building unit 6
Performs processing according to the flow of FIG. Step 601
Then, the equipment setting methods “1. New setting” and “2. Read past model data” are displayed on the display unit 3. Step 6
In 02, the model construction unit 6 takes in the file data of the equipment database 12.

As shown in FIG. 11, the equipment database 12 is made up of building volume, wall, window, air conditioner, lighting and people files. Figure 12 shows the data contents of each file.
To FIG. 17 in order. These file data are respectively displayed as the reference data display portion 655 and used as reference data, as will be described in the subsequent processing. In step 603, the setting method is selected from the options displayed on the display unit 3. Here, when a new model is set, "1" is input from the input unit 1, and when reading a model created in the past, the input unit 1
Enter "2". The case where "1. New setting" is selected will be described first. When "1. New setting" is selected,
Step 604 is executed.

In step 604, the equipment initial screen 651 shown in FIG. 5 is displayed on the display unit 3. In step 605, the operator uses the mouse 1b of the input unit 1 to display the screen 6
When the building icon of the equipment menu 652 of 51 is selected, the building 1 is displayed on the screen 651 with the equipment display unit 653 and its equipment data input unit 654. Here, selecting means pointing device of the input unit 1, that is,
An icon or a character will be picked up by the mouse 1b. The building volume is input from the keyboard 1a. In inputting the building volume, if "building volume" is selected for reference of input, the equipment database 12
The building volume for each building type as shown in FIG. 12, which is read from the table, is displayed as the reference data display unit 655.
In general, since a building is filled with air, the heat capacity C of the building can be calculated from the volume V and the heat capacity c of air per unit volume by the equation (1).

[0038]

## EQU1 ## C = c × V (1) In step 606, the characteristics of the wall of the building are set. When the wall icon is selected from the equipment menu 652 of FIG. 6 to set the wall characteristics, the model building unit 6 controls the equipment data input unit 654 for inputting the wall characteristics on the screen 651 of FIG. It is sent to the unit 2 and displayed on the display unit 3. In FIG. 7, a wall having a thick building is added to the equipment display unit 653. Here, the input of the characteristic of the wall 1 is received. Items to be input are heat transfer coefficient, area, and thickness. When the input item is selected, the reference data of the value of each item is displayed, so that the related data read from the facility database 12 is displayed as the reference data display unit 655. Figure 7
Shows the case where the heat transfer coefficient of the wall 1 is selected, and the heat transfer coefficient per unit area and unit thickness of each material shown in FIG. 13 is displayed. Based on this data, the heat transfer coefficient can be entered. Select other items to display the reference data related to each item. When the heat transfer coefficient per unit area and unit thickness is k1, the area is s1, and the thickness is d1,
The heat transfer coefficient K1 of the wall 1 of this building is given by the equation (2). Further, the heat quantity Q1in that enters the inside of the building from the outside through the wall of this building is given by the equation (3). Here, it is assumed that the temperature outside the building is Tout and the temperature inside the building is Tin. The temperature inside the building is assumed to be uniform within the building.

[0039]

[Equation 2] K1 = k1 × s1 / d1 (2)

[0040]

[Formula 3] Q1in = K1 (Tout-Tin) (3) Further, when the wall icon is selected in the equipment menu 652, the model building unit 6 causes the wall 2 to move outside the wall 1 in FIG. Is added. Assuming that the characteristics of the wall 2 are a unit area, the heat transfer coefficient per unit thickness is k2, the area is s2, and the thickness is d2, the heat transfer coefficient K2 of the wall 1 of this building is given by equation (4), The heat transfer coefficient K of the walls 1 and 2 of
Equation (5) is obtained. At this time, the heat quantity Q2in is given by the equation (6).

[0041]

[Equation 4] K2 = k2 × s2 / d2 (4)

[0042]

[Equation 5] K = 1 / (1 / K1 + 1 / K2) (5)

[0043]

## EQU6 ## Q2in = K (Tout-Tin) (6) The heat transfer coefficient K can be calculated in the same manner when the number of walls increases.

In step 607, the characteristics of the window wall are set. When the window icon is selected from the equipment menu 652 of FIG. 7 to set the window characteristics, the model building unit 6
Causes the screen 651 of FIG. 8 to be displayed on the display unit 3 via the control unit 2. In FIG. 8, a window is added to a part of the wall of the building in the equipment display unit 653. Here, the input of the characteristics of the window 1 is accepted. Items to enter are heat transfer rate, area,
The thickness and the light transmittance. When the input item is selected, the reference data of the value of each item is displayed, so that the related data read from the equipment database is displayed as the reference data display unit 655. FIG. 8 shows the case where the heat transfer coefficient or the transmittance of the window 1 is selected, and is shown in FIG.
The heat transfer coefficient and light transmittance per unit area and unit thickness for each material are displayed. The heat transfer coefficient can be entered based on this data. Select other items to display the reference data related to each item. When the heat transfer coefficient per unit area and unit thickness is k3, the area is s3, and the thickness is d3, the heat transfer coefficient K3 of the window 1 of this building is given by the equation (7). The heat quantity Q3in that enters the inside of the building from the outside through the window of this building is given by equation (8). Here, it is assumed that the temperature outside the building is Tout and the temperature inside the building is Tin.

[0045]

[Equation 7] K3 = k3 × s3 / d3 (7)

[0046]

## EQU00008 ## Q3in = K3 (Tout-Tin) (8) Here, in order to add another window, the same procedure as that for adding a wall can be added. The heat transfer coefficient can be calculated in the same manner when the number of windows is increased.

Since heat enters from the window due to solar radiation, this is further taken into consideration. At this time, the solar radiation transmittance z is input. The heat quantity Q4in due to solar radiation is given by the equation (9), where qsun is the amount of solar radiation per unit area.

[0048]

[Equation 9] Q4in = z × s3 × qsun (9) In step 608, the characteristics of the air conditioner in the building are set. When the air conditioner icon is selected from the equipment menu 652 of FIG. 8 to set the characteristics of the air conditioner, the screen 651 of FIG. 9 is displayed on the display unit 3. In FIG. 9, an air conditioner is added to a part of the wall of the building in the equipment display unit 653. Here, the input of the characteristics of the air conditioner 1 is accepted. Items to be input are capacity and output characteristics. The output characteristic of the air conditioner is a function determined by the internal temperature of the building and the target (set) temperature. Since the characteristics differ depending on the type of air conditioner, "Air conditioner 1
Output characteristics ”, the characteristics for each type read from the equipment database 12 as shown in FIG. 15 are displayed as the reference data display unit 655.

In step 609, the characteristics of the person are set. Equipment menu 6 in FIG. 9 for setting the characteristics of a person
When a person icon is selected from 52, a screen 651 shown in FIG. 10 is displayed.
Is displayed on the display unit 3. In FIG. 10, a person is added inside the building. The input of the characteristic of the person 1 is accepted. The items to be input are the temperature for cooling and the temperature for heating.
That is, when the temperature in the building rises above the temperature for cooling, cooling is turned on, and when the temperature in the building falls below the temperature for heating, heating is turned on. Also here, as the reference data, the data shown in FIG.
Displayed as 5.

Although not set here, other than this, for example, there is a setting of illumination. The data of the amount of heat increasing due to the illumination is given in advance as shown in FIG. 16, for example. This data can also be displayed as reference data.

In step 610, the characteristics of each equipment set in steps 605 to 609 are displayed on the display unit 3 as a list. The display contents are each equipment name and its characteristic value set in steps 605 to 609. In step 611, the input of whether or not the setting is completed is accepted. That is, here, the operator determines whether or not the contents displayed in step 610 are acceptable, and if the contents of the settings are acceptable, the setting completion is input. As a result, in step 612, the input of the file name of the set power consumption model is received from the input unit 1.

At step 613, the model construction unit 6
The power consumption model database 13 stores the power consumption model with the input file name. A file of the power consumption model database 13 is provided for each model name as shown in FIG. Each file contains
Data as shown in is stored. In FIG. 19, the data of the number of adopted days is zero when newly created.
After the number of each facility, the characteristic data of that facility is stored.

When step 613 ends, the switching unit 4
Disconnects the control unit 2 and the model building unit 6, displays an initial screen for mode selection on the display unit 3, and waits for menu selection.

On the other hand, if the past power consumption model is read in step 602, the process proceeds to step 615. In step 615, the name of the power consumption model registered in the power consumption model database 13 and the list of days when the power consumption model is adopted are read out and displayed on the display unit 3. When the displayed power consumption model name is selected in step 616, a file having the selected power consumption model name is read from the power consumption model database 13 in step 617. After reading, the characteristics are displayed on the display unit 3 in step 610. Subsequent processing is processed by the flow of FIG. 3 as already described. If it is desired to change the setting contents in step 611, the model is changed or added by the input unit 1 in step 614.

Details of step 614 are shown in FIG. FIG.
In step 618, the model construction unit 6 causes the display unit 3 to display the equipment menu shown in FIG. 10 and the power consumption model already created. In step 619, the operator's selection as to whether or not to change the model is accepted for the displayed model. Here, if it is selected to change the model configuration, the process proceeds to step 620. In step 620, the equipment in the already created building is selected for the model displayed in step 618. Since the characteristic has already been set, this set value is displayed. For example, on the display screen of FIG. 6, the already set numerical value of the building area is displayed. Step 6
In 21, when the target item is selected, the equipment data is displayed. In the example of FIG. 6, it is the data of the building volume for each building. In step 623, the operator inputs a modification input of the equipment characteristic based on the set value that has already been set and the displayed equipment data from the input unit 1.

If it is selected in step 619 to add the facility without changing the model, the process proceeds from step 624 to step 625. Step 625
Then, in the same way as step 605-step 609,
Add equipment and enter its characteristics. When the equipment is to be deleted, the corresponding equipment is selected in step 627 and deleted in step 628.

As described above, the power consumption model can be created. At the moment, we picked up the air conditioner as a factor that most affects the change in electricity demand.
If the influence of facilities that generate electricity by utilizing solar heat increases, it will be possible to add new types of facilities in the future by using a method that models an air conditioner.

Next, the indoor temperature prediction mode will be described with reference to FIGS. 1, 2 and 20 to 27. The operation of selecting the indoor air temperature prediction mode will be described with reference to FIGS. 1 and 2, and the processing of the indoor air temperature prediction unit 7 will be described with reference to FIGS.
7, respectively.

In order to set the indoor air temperature prediction mode, the operator, as shown in FIG. 2, has menus "1. database edit" and "2. model construction" in the operation mode setting area 315 displayed on the display unit 3. , "3. Indoor temperature prediction",
From "4. Demand analysis", "5. Demand forecast", and "6. Evaluation", "3" (only number) is input from the input unit 1 to select the indoor temperature forecast mode. This mode signal is transmitted to the control unit 2 through the control line D11. Control unit 2
Determines whether the mode input from the input unit 1 matches the indoor temperature prediction mode, and if it determines that the mode matches, instructs the switching unit 4 so that the switch circuit 4a selects the indoor temperature prediction unit 7. , Indoor temperature prediction unit 7 and database 1
1 and 1 are connected. This completes the connection for operating the indoor air temperature predicting unit 7.

This connection relationship continues until the indoor temperature predicting section 7 finishes processing and the indoor temperature predicting section 7 sends a processing end signal to the control section 2 via the control lines S71 and D31. When the control unit 2 receives the processing end signal from the indoor air temperature prediction unit 7, the control unit D31 turns off the switch circuit 4a,
The control unit 2 and the indoor temperature prediction unit 7 are disconnected. At this time, the control unit 2 is not connected to any of the processing units 5-10. As a result, the processing unit 5-10 does not operate unless a new operation mode is input from the input unit 1.

Next, the processing of the indoor temperature predicting section 7 will be described. FIG. 20 shows a flow of the processing operation of the indoor air temperature predicting unit 7. The indoor temperature predicting unit 7 predicts the temperature inside the building (indoor temperature) based on the power consumption model created by the model building unit 6. When the control unit 2 and the indoor temperature prediction unit 7 are connected by the switch circuit 4a, the indoor temperature prediction unit 7
Operates according to the processing flow of FIG. Step 7
In 01, the input of the date for predicting the indoor temperature is accepted via the input unit 1. In step 702, the indoor temperature prediction unit 7 displays on the display unit 3 the name of the power consumption model already stored in the power consumption model database and the list of days when the power consumption model is adopted. Thereby, the operator can select the power consumption model name with reference to the adoption date displayed on the display unit 3. The operator's selection using the input unit 1 is accepted. For example, the power consumption model does not change significantly every day, so
Generally, the name of the power consumption model adopted in the near future will be adopted.

In step 704, the data of the selected power consumption model name is read from the power consumption model database 18. In step 705, the initial value T of the room temperature is
Set in (0) from the input unit 1. In step 706, data is read from the forecast weather database 15 shown in FIG. The weather forecast database 15 includes weather forecast data such as temperature, solar radiation, humidity, discomfort index,
The atmospheric pressure, wind speed, and wind direction are stored as files. These files together make up a tree structure. For example, the temperature file has a year file under the temperature file, a month file under the year file, and a tree structure. Other forecast items have the same structure. FIG. 22 shows an example of the file data for January of the predicted temperature. Temperature at each time, 1
It stores the maximum and minimum temperature of the day and the day of the week. Figure 2
3 is an example of expected solar radiation.

In step 706, the indoor temperature predicting unit 7
Reads the outdoor temperature and the amount of solar radiation in FIGS. 22 and 23. Here, the outdoor temperature is the temperature in a state where it is not exposed to direct sunlight, which is observed inside the Hyakuyoha.

At step 707, the indoor temperature is predicted. Here, the power consumption model is the number of buildings 1, the heat capacity C of the building, the heat transfer coefficient K1 of the wall, the heat transfer coefficient K2 of the window, the light transmittance w, and the air-conditioner characteristic f (the difference between the set temperature and the room temperature). It is assumed to be proportional), and the air-conditioning set temperature Th (t) of the person. Also, t is the time, Qin is the heat entering the building from the outside, Qin
out is the heat discharged from the building to the outside, Tout is the outdoor temperature,
Given that Tin is the room temperature and q is the solar radiation,
From the equations (10) to (13), the room temperature T at each time
You can predict in.

[0065]

[Equation 10] Qin (t) = (K1 + K2) × (Tout (t) −Tin (t)) + w × q (t) (10)

[0066]

[Equation 11] Qout (t) = f × (Tin (t) −Th (t)) (11)

[0067]

## EQU12 ## ΔTin (t) = (Qin (t) -Qout (t)) / C ... (12)

[0068]

[Equation 13] Tin (t + 1) = Tin (t) + ΔTin (t) (13) The above shows an example in which cooling is performed. The air conditioner enters
This is a case where the room temperature Tin is higher than the cooling set temperature Th.
Therefore, when the equation (11) is negative, Qout is set to zero. Similarly, when heating is turned on, the indoor temperature prediction can be predicted.

In step 708, the expected indoor temperature for January is stored in the indoor temperature prediction database 16 as a file with the data structure shown in FIG. That is, the predicted indoor temperature at each time, maximum temperature, minimum temperature, day of the week,
The adopted power consumption model name and the initial value of the indoor temperature are stored in the indoor temperature prediction database 16. The reason why data other than indoor temperature is also stored in the database 16 is
This is because the same state can be reproduced when the indoor temperature is predicted again. The predicted indoor temperature database 16 has a file structure shown in FIG. 24, and stores the predicted indoor temperature and the like on the date entered in step 701.

In step 709, the predicted indoor temperature is displayed on the display unit 3 to present the operator with items that are considered to affect the power consumption. As items to present,
Date when indoor temperature was predicted, predicted indoor temperature change over time, outdoor temperature change over time, maximum temperature and its time of occurrence, minimum temperature and its time of occurrence, time change of solar radiation, time of heat input to the building It is the change over time and the change in heat emitted from the building. 26 and 27 show examples of output screens. In FIG. 26, the predicted time change of the indoor air temperature and the time change of the outdoor air temperature are shown in a graph. In addition, FIG. 27 is a graph showing a temporal change in the amount of solar radiation, a temporal change in heat input to the building, and a temporal change in heat discharged from the building. From these graphs, it is possible to easily grasp the temporal changes in the indoor temperature and the outdoor temperature, the changes in heat input and exhaust heat, and the influence of solar radiation on them.

When step 709 ends, the switching unit 4
Disconnects the control unit 2 from the indoor air temperature predicting unit 7, displays an initial screen for mode selection on the display unit 3, and waits for menu selection. From the above, the indoor temperature can be predicted.

Next, the demand analysis mode will be described with reference to FIGS. 1, 2 and 28 to 40. The operation of selecting the demand analysis mode will be described with reference to FIG. 1, and the processing of the demand analysis unit 8 will be described with reference to FIGS. 28 to 38.

In order to set the demand analysis mode, as shown in FIG. 2, the operation mode setting area 31 displayed on the display unit 3 is displayed.
5 menus "1. Database edit", "2. Model construction", "3. Indoor temperature prediction", "4. Demand analysis",
From "5. Demand forecast" and "6. Evaluation", input unit 1
When "4" (only the number) is input, the control unit 2 selects the demand analysis mode. This mode signal is the control line D11.
Is transmitted to the control unit 2. The control unit 2 determines whether or not the mode input from the input unit 1 matches the demand analysis mode, and when it determines that they match, the switching unit 4
The switch circuit 4a instructs the demand analysis unit 8 to be selected, and connects the demand analysis unit 8 and the database 11.
This completes the connection with each processing unit for the demand analysis unit 8 to operate.

Regarding this connection, the processing of the demand analysis section 8 is completed, and the demand analysis section 8 sends a signal indicating the completion of processing to the control line S81.
It continues until it transmits to the control part 2 via D31. Control unit 2
When receiving the processing end signal from the demand analysis unit 8, the switch disconnects the switch circuit 4a by the control line D31 and disconnects the control unit 2 and the demand analysis unit 8. At this time, the control unit 2 is not connected to any of the processing units 5-10. As a result, unless a new operation mode is input from the input unit 1,
The processing unit 5-10 does not operate.

Next, the processing of the demand analysis unit 8 and the output screen of the display unit 3 accompanying this processing will be described. Although the demand analysis time may be any time, the maximum power demand, which is most important in the demand forecast, is forecast here. Other time zones and minimum powers can be analyzed by similar processing.

FIG. 28 shows a flow of processing operation of the demand analysis unit 8. The demand analysis unit 8 obtains the relationship between the indoor predicted temperature predicted by the indoor temperature prediction unit 7 and the past power demand record. When the control unit 2 and the demand analysis unit 8 are connected by the switch circuit 4a, the demand analysis unit 8 operates according to the processing flow of FIG.

In step 801, the period for analyzing the demand, that is, the input of the first day and the last day from the input unit 1 is accepted. In step 802, the indoor predicted temperature during the demand analysis period is read from the indoor predicted temperature database 16. In step 803, the actual outdoor temperature during the demand analysis period is read from the weather actual performance database 14. here,
The file structure of the meteorological achievement database 14 is as shown in FIG. The weather record database 14 is
The same items as the forecast weather database 15 are stored. FIG. 30 is an example of the actual outdoor temperature for January, in which the temperature at each time, the maximum temperature, the minimum temperature, and the day of the week are stored. Further, FIG. 31 is an example of the actual results of the amount of solar radiation for January, and shows the amount of solar radiation, the maximum amount of solar radiation, and the minimum amount of solar radiation at each time.

In step 804, the demand record of the demand analysis period is read from the power demand record database 18.
Here, the file structure of the demand record database 18 is
As shown in FIG. 32, it has a tree structure. FIG.
Is an example of the demand record for January, and stores demand, maximum demand, minimum demand, and day of the week at each time.

At step 805, the predicted indoor temperature, outdoor temperature, and actual demand read from the database are used to determine the relationship between the actual demand and the indoor temperature and outdoor temperature. Here, when the actual demand P is represented by a function of the outdoor temperature and the indoor temperature, regression analysis is used so as to minimize the average deviation between the actual demand and the predicted value. Here, the demand forecast value Pf
Is expressed by a linear regression equation of the outdoor temperature Tout and the indoor temperature Tin, it is expressed by the equation (14). The error E at this time is represented by Expression (15).

[0080]

[Equation 14] Pf = a × Tout + b × Tin + c (15)

[0081]

[Equation 15] E = Pf−P (16) Here, the variables of the outdoor temperature and the indoor temperature of the meteorological data are used, but the variables used for the analysis can be changed. There are various conceivable ways of using the data of which time. For example, the maximum power demand is used, the indoor temperature starts human activities, and the air conditioner begins to switch on.
The temperature of the hour or 10:00 is used, and the maximum outdoor temperature is used. As another analysis method, it is possible to use a neural network in which the actual demand is used as teacher data and the inputs are indoor temperature and outdoor temperature.

At step 806, the result of the regression analysis is output to the display unit 3. Here, the day of the week to be analyzed is a weekday excluding public holidays from Monday to Friday. The reason for this is that the power demand on Saturdays and Sundays is lower than on weekdays, and it does not make sense to analyze it with weekdays. Whether it is a weekday or not is determined using the data in the day of the week column of the demand record database 18 read in step 804. Here, in the case of public holidays, "holiday" is entered in the column of the day of the week, not the day of the week.

34 to 37 show examples of output screens.
34 shows the relationship between the demand and the indoor temperature, FIG. 35 shows the relationship between the demand and the outdoor temperature, FIG. 36 shows the change in the demand and the outdoor temperature, and FIG. 37 shows the relationship between the indoor temperature and the prediction error (the maximum value of the error, its year and month). Day and average error are also shown). FIG. 38 shows the indoor temperature and the prediction error in a table format. In this state, the operator determines based on the display data in FIG. 38 whether or not there is a large prediction error and unaccepted data.

In step 807, it is determined whether or not there is unaccepted data. This determination is made by comparing with a predetermined reference value. For example, if the standard deviation of the error is σ, the criterion for judging abnormality is 2σ, and if it is more than that, it can be judged as “abnormal”. If there is unaccepted data, in step 808, the demand analysis unit 8 determines that the demand on that day is abnormal data and removes it from the analyzed data. In addition, the demand analysis unit 8
Regarding the data judged to be abnormal, as shown in FIG.
Display the checked status.

Further, the operator can judge that there is an abnormality and remove it from the analysis data. In this case, using the display on the display screen 851 shown in FIG. 38, the input of the presence / absence of unaccepted data is accepted. That is, the icons of “adopted” and “not adopted” are displayed on the display screen 851, and the corresponding icon is selected by the mouse 1 according to the presence or absence of adoption.
This can be done by instructing with b. If there is unaccepted data, check for the corresponding data is accepted.
This can be done by pointing with the mouse 1b.

After removing the abnormal data, the process returns to step 805 to analyze again. Step 80
If there is no unemployed data in step 7, the process proceeds to step 809 to determine whether to change the analysis period. If there is no change in the analysis period, in step 810, the input of the date when the demand forecast model which is the analysis result is stored is accepted.
This demand forecasting model will be used when forecasting the demand on this date. The demand prediction model analyzed in step 811 is stored in the demand prediction model database 17. The demand forecast model database 17 has a file structure shown in FIG. That is, like the other databases described above, it has a tree groove.

FIG. 40 shows an example of file data of the demand forecast model. File data includes the number of variables, variable names,
The order, coefficient, analysis period, non-adoption date, demand forecast value, and rate of decline are stored. At this processing stage, the demand forecast value and the rate of decrease have not been stored yet. Here, the rate of decline is an important rate of decline on Saturdays, Sundays, and public holidays relative to weekday demand. On weekdays, the reduction rate is 1.0.

Even if the date entered in step 810 is Saturday, Sunday, or a national holiday, in step 805, data of weekdays is analyzed. When the analysis period is changed in step 809, the process returns to the first step 801, and the analysis period is input again. When step 810 is completed, the switching unit 4 causes the mode selection unit 2 and the demand analysis unit 8 to operate.
Is disconnected, an initial screen for mode selection is displayed on the display unit 3, and a menu selection waiting state is entered. From the above,
It is possible to analyze the relationship between power demand and outdoor and indoor temperatures.

Next, the demand forecasting mode will be described with reference to FIGS. 1, 2 and 41 to 42. The operation of selecting the demand forecast mode will be described with reference to FIGS. 1 and 2.
In addition, the processing of the demand prediction unit 9 will be described with reference to FIGS. 41 and 42.
Each will be explained.

In order to set the demand analysis mode, the display unit 3
, The operation mode setting area 315 shown in FIG. 2 is displayed, and the selection input from the operator is received. That is, the operator uses the displayed menu "1. Edit database",
From "2. Model construction", "3. Indoor temperature prediction", "4. Demand analysis", "5. Demand prediction", "6. Evaluation", enter "5" (only number) from the input unit 1. Then, the demand forecast mode is selected. The mode signal for selecting "5" input to the input unit 1 is transmitted to the control unit 2 through the control line D11.

The control unit 2 determines whether the mode input from the input unit 1 matches the demand prediction mode. If it is determined that the mode matches the demand prediction mode, the switch circuit 4a selects the demand prediction unit 9 so that the switching unit 4 operates. Then, the demand prediction unit 9 and the database 11 are connected. As a result, the demand forecasting unit 9
Is completed, the connection with each processing unit is completed. This connection relationship continues until the processing of the demand predicting unit 9 ends and the demand predicting unit 9 transmits a processing end signal to the control unit 2 via the control lines S91 and D31. When the control unit 2 receives the processing end signal from the demand prediction unit 9, the control unit 2 disconnects the switch circuit 4a by the control line D31 and disconnects the control unit 2 and the demand prediction unit 9. At this time, the control unit 2 is not connected to any of the processing units 5-10. As a result, the processing unit 5-10 does not operate unless a new operation mode is input from the input unit 1.

Next, the processing of the demand forecasting section 9 and the output screen of the display section 3 accompanying this processing will be described. FIG.
1 shows a flow of processing operation of the demand prediction unit 9. The demand prediction unit 9 predicts the power demand by using the predicted indoor temperature predicted by the indoor temperature prediction unit 7, the predicted temperature of the predicted weather database 15, and the relationship between the demand and the temperature analyzed by the demand analysis unit 8. . When the control unit 2 and the demand prediction unit 9 are connected by the switch circuit 4a, the demand prediction unit 9 operates according to the flow of FIG.

At step 901, the input of the date for predicting the demand from the input unit 1 is accepted. Step 90
In 2, the data of the demand forecast model of the day is read from the demand forecast model database 17 based on the date of demand forecast input in step 901. The data used here are the variable name, order and coefficient. These demand forecasting models are created by the demand analysis unit 8 every day.

At step 903, the data of each time of the forecast target day, that is,
Read expected temperature, maximum temperature, minimum temperature and expected solar radiation. In step 904, the predicted indoor temperature database 1
From 6, the indoor predicted temperature, the maximum temperature, the minimum temperature, and the day of the week at each time of the prediction target day are read.

In step 905, the demand forecast model read in step 902 is substituted with the data read in steps 903 and 904 to forecast the power demand. Here, the read demand forecast model is the variable 3,
When the outdoor maximum air temperature has an order 1, the indoor air temperature has an order 1, and a constant, the power demand prediction value can be calculated by substituting the variable values into the equation (14). Since the order and the variable name are stored in the demand forecast model, for example, when a variable of the amount of solar radiation is added and the number of variables is increased to 4, step 9
03, demand can be predicted by substituting the data read in step 904 for a variable. In step 905, the power demand is calculated assuming that the prediction target day is a weekday.

At step 906, the input of the rate of decrease in demand for non-weekdays on weekdays is accepted via the input unit 1 in order to handle non-weekdays. The rate of decrease in demand is 1.0 on weekdays, but a value smaller than 1 is input on Saturdays, Sundays, and public holidays when the power demand decreases. The rate of decrease is set based on the ratio of the demand forecast to the actual demand based on the weather actual data. For example, when forecasting the demand on Saturday, the ratio of the demand on the nearest Saturday when there is a demand record to the demand on the weekday in the demand forecast model is used. This will also be described in the description of the evaluation unit 10.

In step 907, the display unit 3 displays the calculation results in steps 905 and 906, the power demand forecasting model used, the forecasted weather data, and the estimated indoor temperature. An example of the display screen is shown in FIG. In FIG. 42, the demand forecast value, the date of the forecast target date, the regression equation (coefficient), the rate of decrease, the indoor temperature, and the outdoor temperature are displayed.

At step 908, it is determined whether the predicted result is stored in the database. When storing, the process proceeds to step 909 and the demand forecast model database 17 stores the demand forecast result of the forecast target date. Here, the demand forecast value and the reduction rate are newly added to the data read in step 902. If the demand forecast value and the reduction rate are already stored in step 902, the demand forecast value and the reduction rate will be overwritten in step 909 to be changed. If the demand forecast value predicted in step 909 is not stored, step 906
Then, the reduction rate will be input again. For this reason,
On weekdays, the process does not move from step 908 to step 906.

When step 909 ends, the switching unit 4
Disconnects the control unit 2 from the demand forecasting unit 9, and the display unit 3
The initial screen for mode selection is displayed on the screen, and the unit waits for menu selection. From the above, it is possible to analyze the relationship between the power demand and the outdoor temperature and the indoor temperature.

Next, the evaluation mode will be described with reference to FIGS.
A description will be given with reference to FIGS. 43 to 46. The operation of selecting the evaluation mode will be described with reference to FIGS.
The processing of 0 will be described with reference to FIGS. 43 to 46.

In order to set the evaluation mode, the operator selects the menus "1. database edit", "2. model construction", which are displayed in the operation mode setting area 315 of the display unit 3,
From "3. Indoor air temperature forecast", "4. Demand analysis", "5. Demand forecast", "6. Evaluation", enter "6" (only number) from the input unit 1 and select the evaluation mode. To do. Input section 1
The input mode signal for selecting “6” is the control line D1.
1 to the control unit 2. The control unit 2 determines whether the mode input from the input unit 1 matches the evaluation mode, and when determining that the modes match, the control unit 2 causes the switching unit 4 to switch the switch circuit 4a to the evaluation unit 10 side, and Connect to the database 11. This completes the connection with each processing unit for the evaluation unit 10 to operate.

Regarding this connection, the processing of the evaluation unit 10 is completed, and the evaluation unit 10 sends a signal indicating the completion of the processing to the control lines S101, D.
It continues until it transmits to the control part 2 via 31. The control unit 2
When the processing end signal is received from the evaluation unit 10, the control line D
The switch circuit 4a is turned off by 31 to disconnect the control unit 2 and the evaluation unit 10. At this time, the control unit 2 controls the processing unit 5-
Not connected to any of the 10. With this, unless a new operation mode is input from the input unit 1, the processing unit 5
-10 never works.

Next, the processing of the evaluation section 10 (and the output screen of the display section 3 accompanying this processing) will be described. FIG.
3, FIG. 44, and FIG. 45 show the flow of the processing operation of the evaluation unit 10. The indoor temperature predicting unit 7 and the demand predicting unit 9 predict the indoor temperature and the power demand based on the predicted weather. However, in order to evaluate the demand forecast result, it is also necessary to forecast the power demand using the actual weather record and compare it with the actual power demand record. When the control unit 2 and the evaluation unit 10 are connected by the switch circuit 4a, the evaluation unit 10 operates as shown in FIGS.
4, it operates according to the flow of FIG.

In step 101, the period for analyzing the demand forecast result, that is, the analysis start date and the end date are input from the input unit 1. Next, in step 102, it is determined whether the error of the power demand forecast using the forecast weather data or the error of the power demand forecast using the weather actual data is evaluated. When using the forecast weather data, the process proceeds to step 103. Step 103
Then, from the forecast weather database 15, step 101
Read the expected temperature, solar radiation, etc. for the analysis period input in.
In step 104, from the weather record database 14,
The actual temperature, the amount of solar radiation, etc. during the analysis period input in step 101 are read. Further, in step 105, the demand forecast value using the forecast weather of the analysis period input in step 101 is read from the demand forecast model database 17. In step 106, from the power demand record database 18,
The power demand record of the analysis period input in step 101 is read.

In step 107, the prediction result is evaluated based on the data read in steps 103-106. When the demand forecasting unit 9 forecasts demand, it is possible to evaluate the forecast result for each day because it is for each day. In this case, the daily prediction error is evaluated as large or small. It can also be evaluated over a longer period. That is, the tendency of the prediction result can be grasped by evaluating the prediction error in a slightly longer analysis period such as one week, ten days, and January. The evaluation index is given by a difference (prediction error) between the daily actual demand and the predicted demand value during the analysis period, an average, a variance, a maximum error of the prediction errors, etc., which are calculated in step 107.

Next, in step 108, step 10
Results calculated in step 7 and step 103 to step 10
The data read in 6 is displayed on the display unit 3. An example of this display screen is shown in FIG. In this figure, the analysis period, standard deviation σ, absolute mean error, and
Each data of the maximum error and 2σ, the daily change of the prediction error of the temperature, and the distribution chart showing the daily change of the prediction error of the electric power demand are displayed. This completes the evaluation of the demand forecast error due to the forecast weather.

Next, description will be made regarding the case where the process proceeds to step 109 using the actual weather in step 102. In step 109, the actual temperature is used to predict the indoor temperature.
The detailed processing flow of step 109 is shown in FIG. Further, in step 110, the power demand is predicted based on the indoor temperature predicted in step 109, and the prediction result is evaluated. The detailed processing flow of step 110 is shown in FIG.

At step 111, the data in the indoor temperature prediction database 16 for the analysis period is read. here,
The indoor temperature predicting unit 7 reads the data necessary for reproducing the indoor temperature prediction using the predicted weather when the actual weather is used. Therefore, the initial value of the indoor temperature is read here. Next, in step 112, the power consumption model including the characteristics of the building, the wall, the window, the air conditioner, and the date when this model is adopted are read from the power consumption model database 13. In step 103, the weather record database 1
From 4, read all items such as actual temperature, amount of solar radiation, and humidity. Based on these read data, the indoor temperature is predicted by actual weather.

At step 114, the first day of the analysis period is set to D
Substitute in ay. Next, in step 115, the room temperature on the Day is predicted. Power consumption model database 1
The indoor air temperature is predicted using the power consumption model adopted on the Day from the adoption date of each power consumption model read in 3. As an example, the initial value Tin (0) of the indoor temperature is used, and the power consumption model is the number of buildings 1, the heat capacity C of the building, the heat transfer coefficient K1 of the wall, the heat transfer coefficient K2 of the window, and the light transmittance w. ,
Let the air-conditioner characteristic be f (proportional to the temperature difference between the set temperature and the room temperature), and let the air-conditioning set temperature Th (t) for a person. Also,
t is time, Qin is heat coming into the building from outside, Qout
Is the heat exhausted from the building, Tout is the outdoor temperature, Ti
Let n be the room temperature and q be the solar radiation. Therefore, the indoor air temperature Tin at each time can be predicted from the heat input / output by using the above-described equations (10) to (13).

In step 116, the indoor temperature predicted on the Day is stored under the actual temperature in the indoor temperature prediction database 16. In step 117, the predicted indoor temperature is displayed on the display unit 3. Present the operator with items that may affect power consumption. The items to be presented are the same as in the processing 709 of the indoor temperature prediction unit 7, as shown in FIGS.
As shown in, the predicted indoor temperature, the predicted indoor temperature change over time, the outdoor temperature change over time, the maximum temperature and its time of occurrence, the minimum temperature and its time of occurrence, the change in solar radiation over time, and the building Of the heat input to the building and the heat discharged from the building. When step 117 ends, in step 118, the date of the forecast date is increased by one day to increase Da.
Change day y to (Day + 1) day.

In step 119, it is determined whether or not the Day date is after the end date of the analysis period. If it is after the end date of the analysis period, the indoor temperature prediction process is ended by the actual weather in step 109. If the day of the day is before the analysis period, the process returns to step 115, and the indoor temperature on the day of the day is predicted. Upon completion of step 109, the power demand is predicted and evaluated in step 110 based on the indoor temperature and the actual weather predicted in step 109.

At step 120, the variable name, order, coefficient and reduction rate are read from the demand forecast model database 17. Further, in step 121, all the items of the actual weather such as the actual temperature, the amount of solar radiation, and the humidity during the analysis period and the day of the week are read from the actual weather database 14. In step 122, the first day of the analysis period is substituted into Day.

In step 123, the power demand is predicted by substituting the data read in steps 120 and 121 and the indoor temperature predicted in step 115 into the demand prediction model read in step 120. Here, as in the example described in step 905 of the demand prediction unit 9, it is assumed that the demand prediction model has a variable 3, an outdoor maximum temperature order 1, an indoor temperature order 1 and a constant. In this case, similarly to step 905, by substituting the value of the variable into the equation (14), the power demand forecast value on weekdays can be calculated. In order to correspond to days other than weekdays, the power demand forecast value on weekdays is multiplied by the reduction rate read by the demand forecast model database 17 to obtain the final power demand forecast value.

When step 123 ends, step 1
In 24, the date of the predicted date is increased by one day, and the Day of the day is changed to the (Day + 1) day. In step 125, D
It is determined whether the ay day is after the analysis period end date, and if it is after the analysis period end date, the process proceeds to the evaluation of the prediction result in step 107. In addition, if the Day is before the analysis period,
Returning to step 122, the processing is to predict the power demand on the Day.

When all the data processing of the evaluation section 10 is completed, the switching section 4 disconnects the control section 2 and the evaluation section 10, displays the initial screen for mode selection on the display section 3, and waits for menu selection. It becomes a state. From the above, it is possible to evaluate the predicted value of the power demand when the actual weather is used.

Finally, the database edit mode will be described with reference to FIGS. 1, 2 and 47. The operation of selecting the database editing mode will be described with reference to FIGS. 1 and 2, and the processing of the database editing unit 5 will be described with reference to FIG. To enter the database edit mode,
The operator displays the menus “1. database edit”, “2. model construction”, “3. indoor temperature prediction”, “4. demand analysis” displayed on the operation mode display area 315 of the display unit 3,
From "5. Demand forecast" and "6. Evaluation", input unit 1
Enter "1" (number only) to select the database edit mode. The mode signal selected by the input unit 1 is transmitted to the control unit 2 through the control line D11. Control unit 2
Determines whether the mode input from the input unit 1 matches the database editing mode. If it determines that the mode matches, the switching circuit 4a instructs the switching unit 4 to select the database editing unit 5, The editing unit 5 and the database 11 are connected. This completes the connection with each processing unit for the database editing unit 5 to operate.

This connection relationship continues until the processing of the database editing unit 5 ends and the database editing unit 5 sends a processing end signal to the control unit 2 via the control lines S51 and D31. When the control unit 2 receives the processing end signal from the database editing unit 5, the control unit D 31 disconnects the switch circuit 4 a and disconnects the control unit 2 and the database editing unit 5. At this time, the control unit 2 is not connected to any of the processing units 5-10. As a result, the processing unit 5-10 does not operate unless a new operation mode is input from the input unit 1.

Next, the processing of the database editing unit 5 will be described. FIG. 47 shows a flow of processing operations of the database editing unit 5. The database editing unit 5 adds and edits the data stored in the database 11. When the control unit 2 and the database editing unit 5 are connected by the switch circuit 4a, the database editing unit 5 operates according to the flow of FIG.

At step 501, the input of the database name to be edited from the input unit 1 is accepted. In step 502, the input of the file name in the database to be edited from the input unit 1 is accepted. This file name is a name uniquely determined in the corresponding database. In step 503, the data of the corresponding file is displayed on the display unit 3. If there is no corresponding data file, there is no data to display as the new data file name. In step 504, data is edited or added from the input unit 1 while referring to the data displayed on the display unit 3. When the editing of the corresponding file is completed, the edited and added data file is stored in the database name specified in step 501 with the database name specified in step 502 in step 505.

In step 506, if there is a file to be edited or added in the same database, step 5
Moving to 02, the file name will be input. If there is no file to be edited or added in the same database, move to step 507, edit in another database,
Determine if there are files to add. If there is a file to be edited or added to another database, the process moves to step 501 and the database name is input. If there is no file to be edited or added to another database, the data processing of the database editing unit 5 ends. When the data processing of the database editing unit 5 is completed,
The switching unit 4 disconnects the control unit 2 and the database editing unit 5, displays an initial screen for mode selection on the display unit 3, and waits for menu selection.

As described above, the data items necessary for each database can be stored as the initial data, and the already stored data items can be edited. As a result, it becomes possible to refer to the data necessary for the data processing of the processing unit 6 to the processing unit 10.

In the above, the example of the maximum power demand of one day has been described, but the power demand other than the maximum power demand, for example,
The minimum power demand can be predicted using the indoor temperature in the same way. In the regression analysis of the equations (2) and (3), the maximum temperature and the indoor temperature were used, but the minimum temperature, humidity, weather, etc. stored in the meteorological data 6 are added to perform the regression analysis and also in the demand forecast. , It is possible to predict power demand using these meteorological data. Further, in the data processing of the model building unit 6, an example in which an air conditioner is installed in a building has been described. However, if a power consumption model in which the air conditioner is not installed is created, it is possible to predict the indoor temperature when heat is not emitted by the air conditioner. You can The outdoor temperature is the temperature in the Hyakuyo-box, and is the temperature in the state where it is not directly exposed to sunlight (solar radiation). In a building where people are actually active, the indoor temperature rises due to sunlight. Therefore, by setting the indoor temperature predicted without air conditioners as the outdoor temperature, the temperature inside the building where people are active Can also be used to predict power demand.

Next, the case where the indoor temperature is the actual result will be described with reference to FIGS. 1, 2, and 48. In this case, a sensor necessary for an existing building is attached to collect the actual value of the indoor temperature, and the model building unit 6 is arranged so that the indoor temperature and the predicted value of the indoor temperature predicting unit 7 match.
To help create a power consumption model for. Against such a background, in the future, the environment will be changed to an environment in which a huge amount of transmission data can be collected from each building instead of an optical fiber as a communication cable. At the same time, the air conditioner is equipped with an air temperature sensor for observing the room temperature and controlling the temperature.
By using this temperature sensor, it may be possible to actually measure the indoor temperature of the building without installing a new temperature sensor.

The operation of each processing unit is basically the same as that of the first embodiment. Only changed portions will be described here. First, the configuration will be described with reference to FIG. FIG.
Then, the indoor temperature collecting unit 19 is added as a processing unit that can be connected to the switching unit 4. Along with this, a bus S19 that connects the switching unit 4 and the indoor temperature collecting unit 19 is added. The bus S19 is a signal line S191 and a data bus S1.
And 92. Further, in the database 11, an indoor actual temperature database 20 is added. A data measuring unit 21-11-21-nk is installed in each building.
Each data measuring unit 21 includes an air temperature sensor 22 that measures an indoor air temperature, an air temperature sensor 23 that measures an outdoor air temperature, and a power consumption sensor 24 that measures a power consumption Pa. The indoor temperature collecting unit 19 receives data from these sensors. The indoor air temperature collecting unit 19 uses the signal transmission lines C11-Cnk, which are optical fibers, for the data measuring units 21.
Connected with. Upon receiving the data transmission request from the indoor air temperature collection unit 19, each data measurement unit 21 arranges the data from each sensor and transmits the data to the indoor air temperature collection unit 19 through the optical fiber C11.

Next, the indoor temperature collecting mode will be described with reference to FIGS. 1, 2 and 48. The operation of selecting the indoor air temperature collection mode will be described with reference to FIGS. 1 and 2, and the processing of the indoor air temperature collection unit 19 will be described with reference to FIG.

In order to set the indoor air temperature collection mode, the operator has the menu "1. database edit", "2. model construction", "3. indoor air temperature prediction" displayed in the operation mode setting area 315 of the display unit 3. , "4. Demand analysis",
From "5. Demand forecast", "6. Evaluation", and "7. Indoor temperature collection", "7" (only number) is input from the input unit 1 to select the indoor temperature collection mode. The mode signal selected by the input unit 1 is transmitted to the control unit 2 through the control line D11.

The control unit 2 determines whether or not the mode input from the input unit 1 matches the indoor air temperature collection mode. If it is determined that the mode is matched, the switch circuit 4a selects the indoor air temperature collection unit 19. Then, the switching unit 4 is instructed to connect the indoor temperature collecting unit 19 and the database 11. As a result, the connection with each processing unit for the operation of the indoor air temperature collection unit 19 is completed.

Regarding this connection, the indoor temperature collecting unit 19 finishes the processing, and the indoor temperature collecting unit 19 sends a signal indicating the completion of the processing.
The process continues until it is transmitted to the control unit 2 via the control lines S191 and D31. When the control unit 2 receives the processing end signal from the indoor air temperature collection unit 19, the control line D31 causes the switch circuit 4 to operate.
a is turned off, and the control unit 2 and the indoor temperature collecting unit 19 are disconnected. At this time, the control unit 2 has the processing units 5-10 and 19.
Is not connected to any of the. As a result, the input unit 1
Unless a new operation mode is input from the processing unit 5-1
0, the processing unit 19 does not operate.

Next, the processing of the indoor temperature collecting section 19 will be described. FIG. 48 shows a flow of processing operation of the indoor air temperature collecting unit 19.

In step 1901, the data measuring section 21
Substitute 1 for m in order to obtain the m-th data of. In step 1902, the m-th data measuring unit 2
Output a request signal to send measurement data to 1-m. In step 1903, the measurement data held by the data measuring unit 21-m is received. Here, the received data is the number of the measuring unit, time, power consumption, indoor temperature and outdoor temperature. Upon receiving the measurement data, the indoor air temperature collection unit 19 proceeds to step 1904 where the data measurement unit 2
For 1-m, clear the received data of power consumption, indoor temperature and outdoor temperature at all times. That is,
Make sure there are no data values. For example, if these values are unacceptable values and the temperature is 999 degrees. In step 1905, the number of the measuring unit is increased from m to (m + 1), and in step 1906, it is determined whether it is smaller than the last number k of the data measuring unit. If small,
The process moves to step 1902 again, and the data request signal is output to the data measuring unit of the next number. Step 1
In step 905, when m is larger than the last number k of the data measuring unit 21, the process proceeds to step 1907.

At step 1907, each data measuring unit 2
The average value of the indoor temperature and the outdoor temperature is calculated using the data received from 1. Here, the average value of the room temperature Toutav
In order to calculate, the weight of each measured temperature is obtained. As the simplest method, it is conceivable to divide the total temperature by the number k of the data measuring units 21, that is, to set the weights to be the same. Moreover, the weight of the ratio of the power consumption measured by each data measuring unit 21 to the total sum of the measured power demands can be used. The average value Tinav of the outdoor temperature can be calculated in the same manner.

At step 1908, the average value of the indoor temperature and the outdoor temperature is stored in the indoor actual temperature database 20. Further, the data measuring unit 21 measures the power consumption, the indoor temperature, and the outdoor temperature at a certain sampling period (for example, 1 minute), calculates an average value for each hour, and calculates the power consumption at each time, the indoor temperature, and the Holds outdoor temperature data. If the demand forecast is performed once a day, the maximum time interval for the demand forecast is two days, so data for two days and 48 hours is held.

The data measuring section 21 proceeds to step 1902.
Then, when the data request signal is received from the outdoor air temperature collecting unit 19, the held data of the power consumption amount, the indoor air temperature, and the outdoor air temperature are transmitted to the indoor air temperature collecting unit 19. In addition, in step 1904, when a signal for clearing the held power consumption, indoor temperature, and outdoor temperature data is received,
Clear these held values.

When the data processing of the indoor air temperature collecting unit 19 is completed, the switching unit 4 disconnects the control unit 2 and the indoor air temperature collecting unit 19, displays the initial screen for mode selection on the display unit 3, and displays the menu. Waiting for selection. As described above, the data of the indoor temperature and the outdoor temperature of each building can be collected and stored in the indoor actual temperature database.

Next, in each processing unit, a processing part different from the case where the indoor temperature is predicted will be further described.

In the processing of the database editing unit 5, since the indoor actual temperature database 20 is added, the data in this database 20 can be added and edited. Between step 111 and step 112 of the evaluation unit 10, the indoor temperature actual result and the outdoor temperature actual result of the indoor actual result temperature database 20 are read.

In step 117, the indoor temperature actual result and the outdoor temperature actual result (measurement) are added as items for displaying the prediction result on the display unit 3. Here, if there is a difference between the indoor temperature predicted by the power consumption model and the actual indoor temperature measured by the indoor temperature collection unit 19, the characteristics of the equipment set by the model construction unit 6 are changed. When changing, if the predicted indoor temperature is lower than the measured indoor temperature, and if the heat input from the walls and windows is too small, the countermeasures are to increase the heat transfer coefficient and the volume of the building. It can be seen that can be made smaller. These results can assist the operator in setting the characteristics of the equipment in the model building unit 6.

The power consumption model does not change unless there is a large change in the power consumption mode or the sales volume of air conditioners. In other words, this model should change only for about one week, ten days, or about one month, and it is considered that once it is set well, it can be used for a certain period.

If there is a difference between the outdoor temperature actual data read from the meteorological actual data database 14 and the outdoor actual temperature read from the indoor actual temperature database 20 in step 113, the outdoor temperature actual result of the meteorological actual data database 14 is If you actually use the actual temperature in each building,
It is possible to accurately reflect the usage status of the air conditioner.
Therefore, when the temperature data of the expected weather database 15 and the actual weather database 14 is read and used by each processing unit, the outdoor temperature actual read from the actual weather database 14 and the outdoor actual temperature read from the indoor actual temperature database 20 are used. The difference ΔT can be used by adding the difference ΔT to the temperatures read by the respective processing units of the temperature data of the forecast weather database 15 and the actual weather database 14.

In the above, the data measuring unit 21 measures the power consumption, the indoor air temperature, and the outdoor air temperature, respectively.
However, the present invention is not limited to this. Other sensors can also be provided. For example, a sensor for measuring the amount of solar radiation can be attached to correct the amount of solar radiation in the weather record database 14 and the predicted weather database 15 with the data measured by the data measuring unit 21. By using the amount of solar radiation corrected by each processing unit, it is possible to predict demand using the amount of solar radiation that actually enters the building.

As described above, the indoor air temperature is used to take in the power consumption of the air conditioner, which is the largest fluctuation factor of the power demand at the same time, so that the power demand prediction accuracy can be improved. Furthermore, by using the sensor information installed in the building, it is possible to create a power consumption model that predicts the indoor temperature with higher accuracy. In addition, since the actual indoor temperature can be used when setting the equipment characteristic of the power consumption model, it is possible to assist in setting the equipment characteristic.

Next, a second embodiment of the present invention will be described with reference to FIGS. 2 and 49 to 53.

The second embodiment is different from the first embodiment in that
The weather forecast determining unit 25 and the weather forecast collecting unit 26 are further included. In addition, the forecast weather collecting unit 26 includes a weather forecast source 2
7-1-27-n is connected. Furthermore, the database 11
In addition, the forecast weather database 15-m is added. Against this background, highly accurate weather forecasts are being issued for each specific area, and various actors such as individuals, groups, companies (hereinafter referred to as weather forecast sources) ), But each may come to issue its own weather forecast. Moreover, each weather forecast source has its own characteristics, for example, the accuracy of the weather forecast for summer is good, the accuracy is good when forecasting rain, and the accuracy of the weather forecast for a certain area is good. , It is possible to make use of it to make weather forecasts.

In order to improve the accuracy of power demand prediction,
It is important to select a highly accurate weather forecast from among the weather forecasts to be obtained. Therefore, in the output screen of FIG. 2, when the whole area is checked, the data of the weather forecast source that makes the weather forecast of the entire area is used, and when the area is checked, the data of the weather forecast source for each area is used. I will decide.

The operation of each processing unit of the second embodiment is basically the same as the operation of each processing unit shown in the first embodiment.
Here, only the changed portion will be described. First, the configuration will be described with reference to FIG. 49. In FIG. 49, a predicted weather determination unit 25 and a predicted weather collection unit 26 are added as processing units that can be connected to the switching unit 4. Along with this, a bus S25 connecting the switching unit 4 and the forecast weather determination unit 25
(Signal line S251 and data bus S252) is added,
In addition, a bus S that connects the switching unit 4 and the expected weather collecting unit 26
26 (signal line S261 and data bus S262) are added. The forecast weather collecting unit 26 is a weather forecast source 27-m.
(M is 1 to the number of weather forecast sources) and control line D261-
m and the data bus D262-m. Further, in the database 11, a forecasted weather database 15-m is added. When the weather forecast source 27-m receives the data request signal from the forecast weather collecting unit 26 through the control line D261-m, the forecast weather source 27-m transmits the forecast weather to the forecast weather collecting unit 26 through the data bus D262.

Next, the expected weather collection mode will be described with reference to FIGS. 2, 49, 50 and 51. The operation of selecting the predicted weather collection mode will be described with reference to FIGS. 2 and 49.

The control unit 2 displays the menus “1. database edit”, “2. model construction”, “3. indoor temperature prediction” displayed in the operation mode setting area 315 of the display unit 3,
"4. Demand analysis", "5. Demand forecast", "6. Evaluation",
"7. Forecast weather collection" and "8. Forecast weather determination" are displayed. Note that in this embodiment, 7. Is not "indoor temperature collection" but "expected weather collection", and
This is different from the first embodiment in that “8. Forecast weather determination” is added. The menu of the second embodiment is not shown in FIG. In order to enter the forecast weather collection mode, the operator selects "7" (only the number) from the input section 1 from these menu items to select the forecast weather collection mode. The mode signal for selecting “7” accepted by the input unit 1 is transmitted to the control unit 2 through the control line D11. The control unit 2 determines whether the mode input from the input unit 1 matches the expected weather collection mode. Instruct and connect the forecast weather collecting unit 26 and the database 11. This completes the connection with each processing unit for the expected weather collection unit 26 to operate.

This connection relationship continues until the process of the forecast weather collecting unit 26 ends and the forecast weather collecting unit 26 transmits a signal indicating the process end to the control unit 2 via the control lines S261 and D31. When the control unit 2 receives the processing end signal from the expected weather collection unit 26, the control line D31 causes the switch circuit 4a.
Then, the control unit 2 and the forecast weather collecting unit 26 are disconnected, the initial screen for mode selection is displayed on the display unit 3, and the menu selection waiting state is entered. . At this time, the control unit 2 is not connected to any of the processing unit 5-10, the processing unit 25, and the processing unit 26. Accordingly, unless a new operation mode is input from the input unit 1, the processing unit 5-10 and the processing unit 2
5. The processing unit 26 does not operate.

Next, the processing of the forecast weather collecting section 26 will be described. FIG. 50 shows a flow of processing operations of the forecast weather collecting unit 26. In step 2601, the input of the date for forecasting is accepted. If the demand forecast for the next day is performed every day and the weather forecast is for the next day, the next day of today is the forecast date. In step 2602,
The expected weather collecting unit 26 substitutes 1 into m in order to obtain the m-th data of the weather forecast source 27.

At step 2603, the forecast weather collecting unit 2
6 outputs a request signal to send the weather forecast data to the m-th weather forecast source 27-m. Step 2604
Then, the forecast weather collecting unit 26 receives the forecast data held by the forecast source 27-m. Here, the received data is the temperature, the amount of solar radiation, the humidity, and the like, like the data stored in the expected weather database 15. In step 2605, the forecast weather collection unit 26 stores the weather forecast received on the date entered in step 2601 of the forecast weather database 15-m. In step 2606, the number of the weather forecast source 27 is increased from m to m + 1, and in step 2607, it is determined whether it is smaller than the last number k of the weather forecast source 27. If it is smaller, step 2 again
Moving to 603, the forecast weather collecting unit 26 outputs a data request signal to the weather forecast source 27 having the next number.
In step 2607, if m is larger than the last number k of the weather forecast source 27, the data processing of the forecast weather collecting unit 26 ends. The data stored in the weather forecast source 27 is forecast by each weather forecast source.

In step 2603, when a data request signal is received from the forecast weather collecting section 26, the weather forecast source 27
Is stored, the temperature of the next day, the amount of solar radiation,
The humidity or the like is transmitted to the expected weather collecting unit 26.

Next, the expected weather determination mode will be described with reference to FIGS. 2, 49 and 51 to 55.
The operation of selecting the predicted weather determination mode will be described with reference to FIG.

Similar to the case of the forecast weather collecting section 26,
The menus displayed on the display unit 3 are “1. database edit”, “2. model construction”, “3. indoor temperature prediction”,
"4. Demand analysis", "5. Demand forecast", "6. Evaluation",
From “7. Forecast weather collection” and “8. Forecast weather determination”, enter “8” from the input unit 1 to enter the forecast weather determination mode.
Enter (number only) to select the expected weather determination mode. This mode signal is transmitted to the control unit 2 through the control line D11. The control unit 2 determines whether the mode input from the input unit 1 matches the expected weather determination mode, and when it determines that the mode matches, the switching circuit 4a instructs the switching unit 4 to select the expected weather determination unit 25. Instruct and connect the forecast weather determination unit 25 and the database 11. As a result, the connection with each processing unit for the operation of the predicted weather determination unit 25 is completed.

This connection relationship continues until the process of the predictive weather determination unit 25 ends and the predictive weather determination unit 25 transmits a signal indicating the process end to the control unit 2 via the control lines S251 and D31. When the control unit 2 receives the processing end signal from the forecast weather determination unit 25, the control line D31 causes the switch circuit 4a.
Then, the control unit 2 and the expected weather determination unit 25 are disconnected, the initial screen for mode selection is displayed on the display unit 3, and the menu selection waiting state is entered. At this time, the control unit 2 is not connected to any of the processing unit 5-10, the processing unit 25, and the processing unit 26. As a result, unless a new operation mode is input from the input unit 1, the processing unit 5-10, the processing unit 25,
The processing unit 26 does not operate.

Next, the processing of the forecast weather determining section 25 and the output screen of the display section 3 accompanying this processing will be described. FIG. 51 shows the flow of the processing operation of the forecast weather determination unit 25.

At step 2501, the analysis period is input. In step 2502, the meteorological achievement database 14
Read all data items in the analysis period from. further,
In step 2503, the forecast weather database 15-1
Read all data items from the forecast weather database 15-k during the analysis period. In step 2504, the input of the meteorological data item to be analyzed and the maximum value, the minimum value, or the whole time zone of the item to be analyzed is accepted.

In step 2505, step 2502
Using the data read in step 2503 and the weather data item input in step 2504, which forecast weather database 15-m and weather result database 1
Analyze whether 4 matches well. For example, when the maximum value of the temperature is specified in step 2504, the standard deviation σm and the absolute average deviation gm in the analysis period of the maximum temperature read from the weather record database 14 and the maximum temperature read from the predicted weather database 15-m are calculated. The standard deviation and absolute mean deviation are calculated for m from 1 to the number k of weather forecast sources. For items with only one point per day, calculate the standard deviation and absolute average deviation like maximum temperature. When all the time zones are specified, the standard deviation and absolute average deviation of all the time zones of the analysis period are calculated.

In step 2506, step 2505
Display the analysis results, forecast data and actual data values, etc.
To be displayed. 52 and 53 show examples of analysis result display screens. FIG. 52 shows an example when the maximum temperature is analyzed. The highest temperature changes to the lowest temperature, the highest solar radiation, the highest humidity, etc., depending on the object to be analyzed, but the display format on the screen is the same. FIG. 53 is an example when the temperature (all time zones) is analyzed. Depending on what is analyzed
The display format on the screen is the same, although it changes depending on the humidity. Based on these analysis results, the operator assists in determining which forecast weather database 15-m should be used for the forecast weather used in the processes of the indoor temperature predicting unit 7 and the demand predicting unit 9.

In step 2507, step 2504
It is determined whether to change the maximum and minimum values of the meteorological data item and the item to be analyzed which are input in step 4, or if the time zone is to be changed. Enter the maximum value, minimum value, or all time zone of. If you do not change,
In step 2508, it is determined whether or not the analysis period input in step 2501 should be changed, and if it is changed, the process returns to step 2501. If the analysis period is not changed, the process moves to step 2509.

At step 2509, it is accepted whether or not the forecast weather data is stored in the forecast weather database 15. When storing data, step 251
At 0, the input of the date storing the data is accepted, and at step 2511, the input of the number m of the forecasted weather database 15-m storing the data is accepted. Here, the number of the forecast weather database 15-m to be stored in the forecast weather database 15 is determined based on the analysis result displayed in step 2506. For example, step 250
When the analysis result of the maximum temperature is displayed in 6, it is preferable to select the forecasted weather database 15-m having the smallest standard deviation or absolute average deviation. In Step 2512, Step 25
Step 2 of the forecast weather database 15-m decided in 11
The forecast weather data entered at 510 is read.
In step 2513, the data read in step 2511 is stored in the forecast weather database 15 in step 2510.
Stored as forecasted weather data for the date specified in.

As a result, the data processing of the forecast weather determining section 25 is completed. Even if the data is not stored in step 2509, the data processing of the forecast weather determination unit 25 ends. When the data processing of the weather determination unit 25 is completed, the switching unit 4 disconnects the control unit 2 and the weather determination unit 25, displays an initial screen for mode selection on the display unit 3, and waits for menu selection. .

As described above, a plurality of weather forecast sources 27
The most accurate weather data predicted by the weather forecast source 27 can be selected from. As a result, it is possible to improve the prediction accuracy of the power demand prediction and the prediction accuracy of the indoor temperature.

In each of the above-mentioned embodiments, the fluctuation of the electric power demand due to the use / non-use of the air conditioner is included as a factor of the demand forecast, but the present invention is not limited to this. That is, any electric device whose load changes according to conditions such as weather can be treated in the same manner.

[0164]

As described above, according to the present invention, the indoor temperature is predicted by using the model of the building and taking into consideration the heat input due to the difference between the outdoor temperature and the indoor temperature and the heat input due to the day and summer. be able to. By using this indoor temperature together with the maximum temperature, it is possible to accurately consider the power consumption of the air conditioner, which is the largest fluctuation factor of the power demand at the same time.
This has the effect of improving the power demand prediction accuracy. By improving the prediction accuracy of power demand, there is an effect that the possibility of power failure due to insufficient power supply can be reduced.

[Brief description of drawings]

FIG. 1 is a block diagram showing a system configuration of a first embodiment of a power demand prediction system of the present invention.

FIG. 2 is an explanatory diagram showing an example of a display screen for setting conditions in the system of the present invention.

FIG. 3 is a flowchart showing a process of a model building unit in the system of the present invention.

FIG. 4 is a flowchart showing a data editing procedure of a model building unit.

FIG. 5 is an explanatory diagram showing an example of a display screen for displaying an equipment menu for model building processing.

FIG. 6 is an explanatory diagram showing an example of a display screen for setting a building in a model building process.

FIG. 7 is an explanatory diagram showing an example of a display screen for performing wall setting in model building processing.

FIG. 8 is an explanatory diagram showing an example of a display screen for setting windows for model building processing.

FIG. 9 is an explanatory diagram showing an example of a display screen for setting the air conditioner in the model building process.

FIG. 10 is an explanatory diagram showing an example of a display screen for performing person setting in the model building process.

FIG. 11 is an explanatory diagram showing an example of a file configuration of an equipment database.

FIG. 12 is an explanatory diagram showing an example of volume data for each building type.

FIG. 13 is an explanatory diagram showing an example of heat transfer coefficient data for each wall material.

FIG. 14 is an explanatory diagram showing an example of data of heat transfer coefficient and light transmittance for each window member.

FIG. 15 is an explanatory diagram showing an example of power consumption characteristic data for each air conditioner.

FIG. 16 is an explanatory diagram showing an example of power consumption data for each illumination.

FIG. 17 is an explanatory diagram showing an example of human cooling / heating characteristic data.

FIG. 18 is an explanatory diagram showing an example of a file configuration of a power consumption model.

FIG. 19 is an explanatory diagram showing an example of data of a power consumption model.

FIG. 20 is a flowchart showing a process of indoor temperature prediction.

FIG. 21 is an explanatory diagram showing an example of a file structure of a forecasted weather database.

FIG. 22 is an explanatory diagram showing an example of expected temperature data.

FIG. 23 is an explanatory diagram showing an example of expected solar radiation data.

FIG. 24 is an explanatory diagram showing an example of a file configuration of an indoor predicted temperature database.

FIG. 25 is an explanatory diagram showing an example of data of an indoor predicted temperature database.

FIG. 26 is an explanatory diagram showing an example of an output screen for displaying an indoor air temperature prediction result.

FIG. 27 is an explanatory diagram showing an example of an output screen for displaying the indoor temperature prediction result.

FIG. 28 is a flowchart showing a demand analysis process.

FIG. 29 is an explanatory diagram showing an example of a file configuration of a weather record database.

FIG. 30 is an explanatory diagram showing an example of temperature data.

FIG. 31 is an explanatory diagram showing an example of solar radiation result data.

FIG. 32 is an explanatory diagram showing an example of a file configuration of a demand record database.

FIG. 33 is an explanatory diagram showing an example of demand history data.

FIG. 34 is an explanatory diagram showing an example of an output screen for displaying a regression analysis result.

FIG. 35 is an explanatory diagram showing an example of an output screen for displaying a regression analysis result.

FIG. 36 is an explanatory diagram showing an example of an output screen for displaying a regression analysis result.

FIG. 37 is an explanatory diagram showing an example of an output screen for displaying a regression analysis result.

FIG. 38 is an explanatory diagram showing an example of an output screen for displaying a regression analysis result.

FIG. 39 is an explanatory diagram showing an example of a file configuration of a demand forecast model database.

FIG. 40 is an explanatory diagram showing an example of data of a demand forecast model.

FIG. 41 is a flowchart showing a demand forecasting process.

FIG. 42 is an explanatory diagram showing an example of a display screen for displaying an example of the demand forecast value.

FIG. 43 is a flowchart showing the processing of the evaluation unit.

FIG. 44 is a flowchart showing indoor temperature prediction based on actual data.

FIG. 45 is a flowchart showing demand forecast and evaluation based on actual temperature.

FIG. 46 is an explanatory diagram showing an example of an output screen for displaying an evaluation result.

FIG. 47 is a flowchart showing the processing of the database editing unit.

FIG. 48 is a flowchart showing the processing of an indoor air temperature collection unit.

FIG. 49 is a configuration block diagram of a power demand forecasting system according to a second embodiment.

FIG. 50 is a flowchart showing the processing of the forecast weather collecting unit.

FIG. 51 is a flowchart showing the processing of the forecast weather determination unit.

FIG. 52 is an explanatory diagram showing an example of an output screen for displaying a display result.

FIG. 53 is an explanatory diagram showing an example of an output screen for displaying a display result.

[Explanation of symbols]

DESCRIPTION OF SYMBOLS 1 ... Input part, 2 ... Control part, 3 ... Display part, 4 ... Switching part, 5
... database editing unit, 6 ... model building unit, 7 ... indoor temperature prediction unit, 8 ... demand analysis unit, 9 ... demand prediction unit, 10 ... evaluation unit, 12 ... equipment database, 13 ... power consumption model database, 14 ... weather Actual database, 15 ... Forecast weather database, 16 ... Indoor predicted temperature database, 17 ... Demand forecast model database, 18 ... Electric power demand actual database.

Claims (14)

[Claims] 1. An indoor air temperature predicting unit for predicting an indoor air temperature of a building by a heat input / output model defined using at least a heat input / output characteristic for a building and an outdoor air temperature, and the indoor air temperature predicting unit. It is characterized by having a demand analysis unit that obtains a causal relationship between the indoor air temperature and the power demand predicted by the above, and a demand forecasting unit that predicts an electric power demand using the indoor air temperature based on the obtained causal relationship. Power demand forecasting system. 2. The power demand forecasting system according to claim 1, wherein the demand analysis section performs regression analysis using the indoor temperature and the maximum temperature to obtain a causal relationship. 3. The power demand forecasting system according to claim 1, wherein the demand analysis section performs regression analysis using the indoor temperature, the maximum temperature and the minimum temperature to obtain a causal relationship. 4. The power demand forecasting system according to claim 1, wherein the demand analysis section performs a regression analysis using the indoor temperature, the maximum temperature, the minimum temperature and the humidity to obtain a causal relationship. 5. The electric power demand according to claim 1, wherein the indoor air temperature predicting unit predicts an indoor air temperature of the building by using a heat input / output characteristic of the building, an outdoor air temperature and a daily fee. Prediction system. 6. The power demand prediction system according to claim 1, wherein the demand analysis unit and the demand prediction unit respectively perform regression analysis using the power demand and the indoor temperature to obtain a causal relationship. . 7. The power demand prediction system according to claim 1, wherein the display unit generates a display screen that displays the predicted power demand. 8. The power demand prediction system according to claim 7, wherein the display unit displays the time change of the indoor air temperature predicted by the indoor air temperature prediction unit and the time change of the outdoor air temperature. 9. The power demand prediction system according to claim 7, wherein the display unit displays the indoor temperature predicted by the indoor temperature prediction unit and the demand predicted by the demand prediction unit. 10. The power demand prediction system according to claim 7, wherein the display unit displays a prediction error, which is a difference between the indoor temperature, the actual power demand, and the predicted value. 11. The demand analysis and demand forecast according to claim 1, further comprising: an indoor temperature collecting unit that collects temperature data measured by an air temperature sensor arranged in a building. A power demand forecasting system characterized by performing. 12. The electric power demand prediction system according to claim 11, wherein the air temperature sensor is an air temperature sensor of an air conditioner. 13. The method according to claim 1, 2, 3, 4, 5, 6, 7,
In 8, 9, or 10, the indoor air temperature prediction unit acquires outdoor air temperature and solar radiation amount data from predicted weather data, and predicts the indoor air temperature. 14. The forecast meteorological collection unit for gathering forecasted meteorological data from a plurality of meteorological forecasting sources according to claim 13, and calculating an error between the forecasted meteorological forecast and the actual meteorological forecast forecasted by each meteorological forecasting source. A power demand forecasting system, further comprising: a forecast weather determiner for selecting a weather forecast source having a minimum value.