Electricity demand forecasting device and electricity demand forecasting method

Abstract

To provide a power demand prediction device and a power demand prediction method for predicting a relatively long-term power demand.SOLUTION: A power demand prediction device 1 includes: an extraction unit 12 for extracting power demand predicted for a target period from an inferred value for the power demand for a first observation period of the past observation periods, based on a probabilistic weather forecast value compared with a normal year value; and a presentation unit 14 for presenting the extracted and predicted power demand. The inferred value is a value inferred for the first observation value, which is a meteorological observed value in the first observation period, based on an inference model built in advance.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to an electric power demand forecasting device and an electric power demand forecasting method. [Background technology]

[0002] Conventionally, there have been known techniques for predicting the amount of electricity required in the future in order to ensure a stable supply of electricity, reduce power generation costs, and further reduce environmental impact. It is also known that the electricity demand to be predicted fluctuates due to the influence of natural factors such as weather. In recent years, electricity demand prediction models have been developed using mathematical techniques based on various data on such natural factors.

[0003] For example, Patent Document 1 discloses a technique for predicting the power demand on a target day by utilizing the correlation of the shape of annual fluctuations in past monthly power consumption. Patent Document 2 discloses a technique for predicting the power demand on a target day by using a neural network to build a prediction model using past power demand performance data and meteorological information. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Patent No. 4261471 [Patent Document 2] Patent No. 3707589 Summary of the Invention [Problem to be solved by the invention]

[0005] However, according to conventional techniques, the period for forecasting power demand is relatively short, and it is difficult to forecast power demand for a relatively long period, for example, one month or more in the future.

[0006] The present invention has been made to solve the above-mentioned problems, and has an object to predict power demand over a relatively long period of time. [Means for solving the problem]

[0007] In order to solve the above-mentioned problems, the power demand forecasting device of the present invention includes an extraction unit that extracts a power demand predicted for a target period from an inferred value of power demand for a first observation period among past observation periods based on a probabilistic weather forecast value compared with an average year value, and a presentation unit that presents the extracted predicted power demand, wherein the inferred value is a value inferred for a first observation value, which is an observed value of weather for the first observation period, based on an inference model constructed in advance.

[0008] In addition, the power demand forecasting device of the present invention may further include an inference unit that inputs the first observation value observed in the first observation period into the inference model constructed by learning the relationship between a second observation value, which is a weather observation value in a second observation period among the past observation periods, and an actual measured value of power demand, and calculates the inference model to determine the inferred value of power demand in the first observation period, and the inferred value determined by the inference unit may be input to the extraction unit.

[0009] In addition, the power demand forecasting device of the present invention may further include a learning unit that learns the relationship between the second observation value and the actual measured value during the second observation period to construct the inference model, wherein the second observation period is set according to information regarding the seasonal classification of the target period, and the inference unit may read out the inference model from the learning unit and perform calculations of the inference model.

[0010] In addition, in the power demand forecasting device of the present invention, the observed weather values ​​are temperature observed values ​​divided into a plurality of classes depending on whether the values ​​are high or low relative to the normal value, which is the average value of temperatures during the past observation period, and the probabilistic weather forecast values ​​compared to the normal value are expressed as a probability that the temperature distribution related to the forecast falls into each of the plurality of classes, and the extraction unit may extract the predicted power demand using an average value of the inferred value corresponding to a possible class of the first observed value in the first observation period, weighted by the probability of each of the plurality of classes indicated by the forecast value.

[0011] In order to solve the above-mentioned problems, the power demand forecasting method of the present invention includes an extraction step of extracting a power demand predicted for a target period from an inferred value of power demand for a first observation period among past observation periods based on a probabilistic weather forecast value compared with a normal value, and a presentation step of presenting the extracted predicted power demand, wherein the inferred value is a value inferred for a first observation value, which is an observed value of weather for the first observation period, based on an inference model constructed in advance.

[0012] In addition, the electricity demand forecasting method of the present invention may further include an inference step of inputting the first observation value observed in the first observation period into the inference model constructed by learning the relationship between a second observation value, which is a weather observation value in a second observation period among the past observation periods, and an actual measured value of electricity demand, and calculating the inference model to obtain the inferred value of electricity demand in the first observation period, and the inferred value obtained by the inference step may be used in the extraction step.

[0013] In addition, the electricity demand forecasting method of the present invention further includes a learning step of learning the relationship between the second observation value and the actual measured value during the second observation period to construct the inference model, the second observation period being set according to information regarding the seasonal classification of the target period, and the inference step may read out the inference model constructed in the learning step from a memory unit and perform calculations of the inference model. Effect of the Invention

[0014] According to the present invention, since prediction is made based on probabilistic weather forecast values ​​compared with normal values, it is possible to predict power demand over a relatively long period of time. [Brief description of the drawings]

[0015] [Figure 1] FIG. 1 is a block diagram showing a configuration of a power demand forecasting system including a power demand forecasting device according to a first embodiment of the present invention. [Diagram 2] FIG. 2 is a block diagram illustrating a hardware configuration of the power demand prediction device according to the first embodiment. As illustrated in FIG. [Diagram 3] FIG. 3 is a diagram for explaining an example of a long-term forecast input to the power demand prediction device according to the first embodiment. [Figure 4] FIG. 4 is a diagram for explaining an example of weather data used in the power demand forecasting device according to the first embodiment. [Diagram 5] FIG. 5 is a flowchart for explaining the operation of the power demand prediction device according to the first embodiment. [Figure 6] FIG. 6 is a diagram for explaining an example of a prediction result of power demand presented by the power demand prediction device according to the first embodiment. [Figure 7] FIG. 7 is a diagram for explaining an example of a prediction result of power demand presented by the power demand prediction device according to the first embodiment. [Figure 8] FIG. 8 is a block diagram showing a configuration of a power demand prediction system including a power demand prediction device according to the second embodiment. [Figure 9] FIG. 9 is a flowchart for explaining the operation of the power demand prediction device according to the second embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0016] A preferred embodiment of the present invention will be described in detail below with reference to Figures 1 to 9. In this embodiment, the observed and forecast values ​​of weather will be described by taking as an example a case where the observed and forecast values ​​of temperature are taken.

[0017] [First embodiment] 1 is a block diagram showing a configuration of a power demand forecasting system including a power demand forecasting device 1 according to a first embodiment of the present invention. The power demand forecasting system according to this embodiment predicts power demand for a relatively long period of time, up to one month or more ahead, based on weather forecast values, which are probabilistic long-term weather forecasts compared with normal values.

[0018] 1, the power demand forecasting system includes a weather forecast data storage unit 10, a weather data storage unit 11, and a power demand forecasting device 1. The power demand forecasting device 1 is connected to the weather forecast data storage unit 10 and the weather data storage unit 11 via a network NW. The weather forecast data storage unit 10 and the weather data storage unit 11 are, for example, databases provided in an external weather information system.

[0019] The power demand prediction device 1 obtains a probabilistic weather forecast, which is compared with the average value for the target period for which the power demand is predicted, from the weather forecast data storage unit 10. The weather forecast stored in the weather forecast data storage unit 10 is a long-term forecast of the temperature at the location or geographical area of ​​the facility to be predicted. For example, a three-month seasonal forecast issued by the Japan Meteorological Agency can be used.

[0020] Furthermore, the power demand forecasting device 1 acquires meteorological observation values ​​(first observation values) for a set first observation period among past observation periods from the meteorological data storage unit 11. The meteorological observation values ​​stored in the meteorological data storage unit 11 are data on temperature observed at the location or geographical area of ​​the facility to be forecasted. For example, temperature observation data collected by AMeDAS (Automated Meteorological Data Acquisition System) for the past 30 years can be used as the first observation values ​​for the first observation period.

[0021] [Functional blocks of the power demand forecasting device] The power demand prediction device 1 includes an extraction unit 12, an inference unit 13, a presentation unit 14, and a first storage unit 15. The power demand prediction device 1 is installed, for example, in a facility whose power demand is to be predicted, and predicts a relatively long-term power demand in the facility up to three months ahead.

[0022] The extraction unit 12 extracts the power demand predicted for the target period from the inferred value of the power demand for a first observation period among past observation periods based on the probabilistic weather forecast value compared with the normal year value. The extraction unit 12 extracts the predicted power demand using the weather forecast value acquired from the weather forecast data storage unit 10. As described above, in this embodiment, the inferred value for the past 30 years is used as the inferred value for the first past observation period.

[0023] The probabilistic weather forecast values ​​compared to normal values ​​used by the extraction unit 12 are long-term forecasts such as one-month forecasts or three-month forecasts of temperature at the location or geographical area of ​​the facility to be predicted. The forecast period of the long-term forecast corresponds to the length of the forecast period for which the power demand is to be predicted. For example, in this embodiment, the extraction unit 12 performs extraction processing based on a three-month forecast of temperature to extract power demand for the next three months.

[0024] FIG. 3 is a schematic diagram showing an example of a probabilistic weather forecast value compared to the normal value used by the extraction unit 12. (a), (b), and (c) of FIG. 3 show a three-month forecast of temperature for each month. The probabilistic temperature forecast value compared to the normal value is divided into multiple classes according to the distribution of the forecasted temperatures relative to the normal value. The normal value is the average of the observed temperature data for the past 30 years. Furthermore, the temperature is divided into three classes relative to the normal value, namely, "high" which is higher than the normal value, "average" which is the same as the normal value, and "low" which is lower than the normal value.

[0025] Figure 3 (a) to (c) show, using shades of color, the probability that temperatures in each prefecture one month, two months, and three months from now will be in one of three categories: "high," "average," or "low," compared to the average temperature for that month.

[0026] As shown in Figure 3(a), for example, in Hokkaido, the probability that the temperature forecast value one month ahead will be lower than the normal value is 10%, that it will be normal to the normal value is 30%, and that it will be higher than the normal value is 60%. Also, as shown in Figure 3(a), in each prefecture of Honshu one month ahead, the probability that it will be lower than the normal value is 20%, that it will be normal to the normal value is 40%, and that it will be higher than the normal value is 40%. Similarly, Figure 3(b) shows the probability that the temperature two months ahead will be in the "high", "average", and "low" classes, and Figure 3(c) shows the probability that the temperature three months ahead will be in the "high", "average", and "low" classes.

[0027] In this way, the extraction unit 12 uses the probability value of the temperature forecast for three months ahead in the area of ​​the facility to be predicted as an input. For example, in the example of the three-month temperature forecast shown in (a) to (c) of FIG. 3, consider a case where electricity demand is predicted for a facility located in Hokkaido from March 2021 up to three months ahead. In this case, the extraction unit 12 uses the forecast values ​​of the temperature for April 2021 "Low: Average: High (%): 10: 30: 60", the forecast values ​​of the temperature for May 2021 "Low: Average: High (%): 20: 40: 40", and the forecast values ​​of the temperature for June 2021 "Low: Average: High (%): 20: 30: 50" as input.

[0028] 1, the extraction unit 12 extracts the power demand predicted in the prediction target period by using an inferred value of the power demand inferred in advance for the observation data (first observation value) of the temperature in the past 30 years. In this embodiment, the extraction unit 12 uses the inferred value of the power demand in the past 30 years obtained by the inference unit 13 described later.

[0029] The inference unit 13 uses a previously constructed inference model 15a to determine an inferred value of power demand for meteorological observation data over the past 30 years. Although details will be described later, the inference model 15a is a model that is constructed by prior learning of the relationship between the observed temperature data (second observed value) during a set second observation period among past observation periods and the actual measured value of power demand. The inference unit 13 inputs meteorological values ​​observed over the past 30 years into the inference model 15a, and calculates the inference model 15a to determine an inferred value of power demand over the past 30 years.

[0030] More specifically, the inference unit 13 provides the inference model 15a with the observation data of temperature for the past 30 years acquired from the weather data storage unit 11 of an external weather information system as unknown input data, and determines the value of the power demand to be inferred for the past 30 years in the facility to be predicted. The inference model 15a is constructed in advance by learning the relationship between past temperatures and power demand, for example, on an external server, and is stored in the first storage unit 15.

[0031] The presentation unit 14 presents the power demand predicted for the target period extracted by the extraction unit 12. For example, the presentation unit 14 displays the prediction result of the power demand for each set period, such as each month, on the display device 107 included in the power demand prediction device 1.

[0032] The first storage unit 15 stores average values ​​of temperature and values ​​related to class divisions used when the extraction unit 12 extracts the value of the predicted power demand. The first storage unit 15 also stores a pre-constructed inference model 15a used by the inference unit 13 for inference processing. The first storage unit 15 stores actual measured values ​​of power demand in the facility to be predicted. Furthermore, the first storage unit 15 stores information related to data processing of the prediction result when the presentation unit 14 displays the predicted power demand.

[0033] Here, a process in which the extraction unit 12 extracts the power demand predicted for the next three months (April to June 2021) in the prediction target facility will be described in more detail with reference to FIG. 4 as an example.

[0034] In this embodiment, the extraction unit 12 extracts the average value of the power demand taking into account probability, i.e., the value of the power demand predicted for the prediction period, from the inferred value of the power demand for the past 30 years using expected value calculation.

[0035] Data a in FIG. 4 shows observed data of temperature for the past 30 years acquired from the weather data storage unit 11 and the corresponding inferred value of power demand. Specifically, in the example of data a in FIG. 4, inferred values ​​of power demand arranged in ascending order of temperature in April for the past 30 years (1991 to 2020) are shown. As described above, in this embodiment, temperatures are classified into three classes "high", "average", and "low" in comparison with the average value. In the example of data a in FIG. 4, the temperature for the top 10 years is classified into the class "low", the temperature for the middle 10 years is classified into the class "average", and the temperature for the bottom 10 years is classified into the class "high". In addition, in data a in FIG. 4, the area surrounded by a dashed rectangle shows the inferred value of power demand associated with the three classes "high", "average", and "low" classification of the average value of temperature.

[0036] Data b in Fig. 4 is one month's value of the long-term forecast of temperature up to three months ahead acquired by the extraction unit 12 from the weather forecast data storage unit 10 as described in Fig. 3, and shows the temperature probability by class. That is, Fig. 4 shows a long-term forecast in which, for example, in the area of ​​the facility to be predicted, the temperature one month ahead, i.e., in April 2021, has a 70% probability of being higher than average ("High: 70%), a 20% probability of being average ("Average: 20%), and a 10% probability of being lower than average ("Low: 10%).

[0037] The extraction unit 12 uses a certain inferred value according to the probability of the long-term forecast of temperature from among the inferred values ​​of power demand based on the observation data of temperature over the past 30 years for the expected value calculation. More specifically, the extraction unit 12 uses a number of inferred values ​​of power demand according to the probability values ​​of the long-term forecast of temperature in April 2021 shown in data b of Fig. 4 ("High: 70%", "Average: 20%", "Low: 10%) for the expected value calculation using formula c of Fig. 4.

[0038] More specifically, the extraction unit 12 adjusts the range of the inferred power demand value selected from each class division according to the magnitude of the probability value of each class indicated by the long-term temperature forecast. For example, when the probability value of the class "high" is the highest among the probability values ​​of the three classes of the long-term temperature forecast, the extraction unit 12 selects the inferred power demand value with the higher temperature value in each of the three classes "high", "average", and "low" from each of the three classes "high", "average", and "low" and uses it for the expected value calculation. Therefore, in this case, among the inferred power demand values ​​belonging to the class "high", the inferred power demand value associated with the higher temperature value is selected. Also, among the inferred power demand values ​​belonging to the class "average", the inferred power demand value associated with the higher temperature value is selected. Similarly, among the inferred power demand values ​​belonging to the class "low", the inferred power demand value associated with the higher temperature value is selected.

[0039] On the other hand, when the probability value of the "low" class is the highest among the probability values ​​of the three classes of the long-term temperature forecast, the extraction unit 12 selects the inferred power demand value with the lower temperature value among the three classes of inferred power demand values ​​corresponding to the temperatures of the observation data for the past 30 years, and calculates the expected value. Therefore, in this case, the inferred power demand value associated with the lower temperature value among the inferred power demand values ​​belonging to the "high" class is selected. Also, the inferred power demand value associated with the higher temperature value is selected among the inferred power demand values ​​belonging to the "average" class. Similarly, the inferred power demand value associated with the lower temperature value is selected among the inferred power demand values ​​belonging to the "low" class.

[0040] Using the example of Figure 4, the probability value of the long-term forecast of temperature indicated by data b is the highest at "70%" for the "high" class. For this reason, as shown by the dotted arrow "7 years" in Figure 4, the inferred value of power demand for 7 years is selected from the data classified as "high" class included in data a, starting from the data with the highest temperature values. Also, as shown by the dotted arrow "2 years" in Figure 4, the inferred value of power demand for 2 years is selected from the data classified as "average" class included in data a, starting from the data with the highest temperature values. Similarly, as shown by the dotted arrow "1 year" in Figure 4, the inferred value of power demand for 1 year is selected from the data classified as "low" class, starting from the data with the highest temperature values.

[0041] In this way, the extraction unit 12 extracts the power demand predicted in the forecast target period by using the average value, that is, the expected value, of the inferred power demand corresponding to the classes "high", "average", and "low" that the temperature observation data values ​​for the past 30 years can take, weighted by the probability of each of these three classes indicated by the long-term temperature forecast. As represented by formula c in FIG. 4, the predicted power demand is extracted by calculating the expected value = {[inferred value of power demand for 7 years selected from the class "high"] + [inferred value of power demand for 2 years selected from the class "average"] + [inferred value of power demand for 1 year selected from the class "low"] / [10 years]}. In addition, similar to the calculation of the expected value for April 2021 shown in FIG. 4, the expected values ​​can be calculated for May and June 2021, respectively, to extract the power demand predicted in the forecast target period.

[0042] As described above, the extraction unit 12 performs extraction processing including a process of adjusting the range of the inferred value of the power demand selected from each class division according to the magnitude of the probability value of each class indicated by the long-term temperature forecast, and a process of calculating the expected value based on the selected inferred value of the power demand. Note that the extraction unit 12 may calculate the expected value by using the average value of the inferred value of the power demand included in each class division.

[0043] In the above-mentioned extraction unit 12, a case where a long-term forecast for up to three months ahead is used on a monthly basis for forecast data in the range from the first day to the last day of each month is exemplified. However, the specific range of the forecast for one month is not limited to the case where it is from the first day to the last day of the month. For example, the range of forecast data for April may be from March 28 to April 27. In this case, the extraction unit 12 can use, for example, past observation data of temperature from March 28 to April 27 from 1991 to 2020 as observation data for the past 30 years, and extract the power demand predicted from March 28 to April 27, 2021.

[0044] [Hardware configuration of the power demand forecasting device] Next, an example of a hardware configuration for realizing the power demand prediction device 1 having the above-mentioned functions will be described with reference to FIG.

[0045] 2, the power demand prediction device can be realized by, for example, a computer including a processor 102, a main memory device 103, a communication interface 104, an auxiliary memory device 105, and an input / output (I / O) 106, which are connected via a bus 101, and a program that controls these hardware resources. In addition, the power demand prediction device 1 can include a display device 107 connected via the bus 101.

[0046] The main memory device 103 stores in advance programs for the processor 102 to perform various controls and calculations. The processor 102 and the main memory device 103 implement the functions of the extraction unit 12 and the inference unit 13 shown in FIG.

[0047] The communication interface 104 is an interface circuit for networking the power demand prediction device 1 and various external electronic devices. The communication interface 104 may be configured to receive weather forecast data and past weather data used by the extraction unit 12 described in Fig. 1 and the inference model 15a used by the inference unit 13 from an external terminal via the network NW.

[0048] The auxiliary storage device 105 is composed of a readable / writable storage medium and a drive for reading and writing various information such as programs and data from and to the storage medium. The auxiliary storage device 105 can use a semiconductor memory such as a hard disk or a flash memory as the storage medium.

[0049] The auxiliary storage device 105 has a program storage area for storing the power demand forecasting program executed by the power demand forecasting device 1. The auxiliary storage device 105 realizes the first storage unit 15 described in FIG. 1. The auxiliary storage device 105 also stores information on the average year temperature, the high and low temperature classifications compared to the average year temperature, and information on a formula for calculating the expected value. Furthermore, the auxiliary storage device 105 may have, for example, a backup area for backing up the above-mentioned data, programs, and the like.

[0050] The input / output I / O 106 is composed of an I / O terminal for inputting a signal from an external device and outputting a signal to an external device.

[0051] The display device 107 is configured by an organic EL display, a liquid crystal display, etc. The display device 107 can also realize the presentation unit 14 described in FIG.

[0052] Here, the program stored in the program storage area of ​​the auxiliary storage device 105 may be a program that is processed in time series according to the order of the power demand forecasting method described in this specification, or may be a program that is processed in parallel or at a required timing such as when called. Also, the program may be processed by one computer, or may be processed in a distributed manner by multiple computers.

[0053] [Operation of the power demand forecasting device] Fig. 5 is a flowchart showing the operation of the power demand forecasting device 1 according to this embodiment. First, the extraction unit 12 inputs a probabilistic weather forecast value compared with an average value, which is acquired, for example, via the network NW from the weather forecast data storage unit 10 of an external weather information system (step S1). Specifically, the extraction unit 12 inputs a long-term forecast of temperature for the next three months acquired from the weather forecast data storage unit 10. Note that in step S1, the extraction unit 12 is not limited to inputting a forecast value acquired via the network NW, and may be configured to read and input a forecast value previously stored in a portable memory such as a USB memory.

[0054] Next, the inference unit 13 provides the inference model 15a read out from the first memory unit 15 with the temperature observation data for the past 30 years as unknown input, performs calculations according to the inference model 15a, and obtains an inferred value of electricity demand based on the temperature observation data for the past 30 years (step S2).

[0055] Next, the extraction unit 12 uses the probability value of the long-term forecast of temperature input in step S1 to calculate an expected value for the inferred value of the power demand obtained in step S2 (step S3). Specifically, when the probability values ​​of each class for a certain month in the long-term forecast of temperature input in step S1 are "high": 70%, "average": 20%, and "low": 10%, the extraction unit 12 calculates an average value of the inferred values ​​of the power demand for the past 30 years obtained in step S2 weighted by each probability of "high": 70%, "average": 20%, and "low": 10%.

[0056] Furthermore, when calculating the expected value in step S3, if the probability value of the long-term temperature forecast is the highest for the "high" class among the three classes of "high," "average," and "low," the extraction unit 12 can select the inferred value of the electricity demand for each class from the data for the higher temperature within each of these classes of "high," "average," and "low."

[0057] In addition, when the probability value of the long-term temperature forecast is the highest among the three classes of "high", "average", and "low", the extraction unit 12 can select the inferred value of the electricity demand for each class from the data for the lower temperature within each of these classes of "high", "average", and "low".

[0058] In this way, the extraction unit 12 adjusts the range of inferred values ​​of power demand selected from each class interval depending on the magnitude of the probability value of each class indicated by the long-term temperature forecast.

[0059] Next, the extraction unit 12 obtains the power demand predicted for the prediction period from the expected value calculated in step S3 (step S4). Specifically, the extraction unit 12 can calculate the expected value in step S3 according to the forecast period related to the long-term forecast. For example, if the long-term forecast is a three-month forecast for each month, the extraction unit 12 can calculate the expected value in step S3 for each month and obtain the power demand for up to three months ahead.

[0060] Next, the presentation unit 14 presents the result of the prediction of the power demand obtained in step S4 on the display device 107 (step S5).

[0061] 6 and 7 are diagrams showing examples of the results of forecasting electricity demand presented by the presentation unit 14. In an area d in Fig. 6, for each of the forecast target periods "April," "May," and "June," "Forecast result (kWh)," "Last year's performance (kWh)," and "Compared to last year" are displayed in association with each other.

[0062] FIG. 7 shows an example of the prediction result of FIG. 6 displayed by the display device 107 in a bar graph for each month. The horizontal axis of FIG. 7 indicates the prediction target periods of "April 2021," "May 2021," and "June 2021," and the vertical axis indicates the amount of power [kWh]. Data e of the bar graph represents the actual value of power demand last year, and data f represents the value of power demand in the prediction result. As shown in FIG. 7, by displaying the power demand for the next three months in a form that can be compared with the actual value of last year, it becomes easier to grasp the long-term power demand.

[0063] The presentation unit 14 can further display the predicted power demand in a graph, separated for each individual section or for each set of sections included in the prediction target facility. Alternatively, the presentation unit 14 may display the power demand predicted at multiple points in a graph as prediction data for a wider area.

[0064] In the embodiment described above, the inference unit 13 reads out the inference model 15a constructed in advance from the first storage unit 15 and performs the inference processing. However, the inference processing by the inference unit 13 may be configured to be performed by an external server. In this case, the extraction unit 12 obtains the inferred value of the power demand from the external server via the network NW. Also, in this case, the inference model 15a is stored in the external server.

[0065] As described above, according to the power demand forecasting device 1 of the first embodiment, power demand is forecast based on a long-term probabilistic weather forecast compared with normal values, and therefore it is possible to forecast power demand over a relatively long period of time.

[0066] In addition, the power demand forecasting device 1 uses an average value of inferred values ​​of power demand over the past 30 years that correspond to the possible classes of temperature observation data over the past 30 years, weighted by the probability of each of multiple classes indicated by the long-term forecast, making it easier to forecast power demand for relatively long periods of time, up to one month or more in the future.

[0067] [Second embodiment] Next, a second embodiment of the present invention will be described. In the following description, the same components as those in the first embodiment are given the same reference numerals, and the description thereof will be omitted.

[0068] In the first embodiment, the extraction unit 12 acquires an inferred value of the power demand for a first observation period among past observation periods, that is, for the past 30 years, which is inferred in advance by the inference unit 13. Also, the inference unit 13 reads out an inference model 15a constructed in advance from the first storage unit 15 and performs inference processing. In contrast, in the second embodiment, the inference unit 13 further includes a learning unit 16 and performs learning processing of the inference model 15a.

[0069] As shown in FIG. 8, the power demand prediction system according to this embodiment includes a power demand prediction device 1A, a weather forecast data storage unit 10, a weather data storage unit 11, and a learning data storage unit 17.

[0070] [Functional blocks of the power demand forecasting device] As shown in Fig. 8, the power demand forecasting device 1A according to this embodiment includes an extraction unit 12, an inference unit 13, a presentation unit 14, a first storage unit 15, and a learning unit 16. The power demand forecasting device 1A according to this embodiment differs in configuration from the power demand forecasting device 1 according to the first embodiment in that the power demand forecasting device 1A according to this embodiment includes the learning unit 16. The following description will focus on the configuration that differs from the first embodiment.

[0071] The learning unit 16 learns the relationship between the second observation value, which is the observation value of the temperature in a second observation period that is set in advance among the past observation periods, and the actual measured value of the power demand in the second observation period, and constructs the inference model 15a. The second observation period is set according to information on the seasonal division of the prediction target period, and specifically, is set according to the season and length of the prediction target period. For example, the learning unit 16 can use the observation data of the temperature for five months or one year of the previous year and the actual measured value of the power demand at the prediction target facility as learning data according to the season and length of the prediction target period. In addition, when the extraction unit 12 predicts the power demand from April to June 2021, the observation data of the temperature and the actual measured value of the power demand for five months of the previous year from March to July 2020 can be used as learning data.

[0072] Here, the second past observation period, which is the period for acquiring learning data, will be described. As described above, the first past observation period is a target period for which the inference unit 13 performs inference processing to obtain an inferred value of power demand, and in this embodiment, an inferred value is obtained for observation data of temperature over the past 30 years. On the other hand, the second past observation period for acquiring learning data is set according to the season and length of the prediction target period as described above, and can be a past period that does not overlap with the first past observation period.

[0073] Specifically, the second past observation period may not overlap with the first past observation period, may be partly included in the first past observation period, or may be included in the first past observation period. For example, when predicting electricity demand in 2025, the 30 years from 1991 to 2020 may be used as the first observation period, and the observation data of temperature in 2024 may be used as the second observation period related to the learning data.

[0074] The learning unit 16 acquires learning data for constructing the inference model 15a from the learning data storage unit 17, and performs a learning process using a known machine learning algorithm. In addition, every time the past observation data of temperature used as the learning data is updated in the weather information system, the learning unit 16 can re-learn and construct the inference model 15a based on the updated weather data.

[0075] The learning data storage unit 17 stores learning data used by the learning unit 16 to construct the inference model 15a. More specifically, the learning data storage unit 17 can store learning data consisting of observation data of temperature in the previous year and actual measured values ​​of power demand in the facility to be predicted in the previous year.

[0076] [Operation of the power demand forecasting device] Next, the operation of the power demand forecasting device 1A having the above-mentioned configuration will be described with reference to the flowchart in Fig. 9. First, the extraction unit 12 inputs a probabilistic weather forecast value compared with an average year value, which is acquired from the weather forecast data storage unit 10 via the network NW (step S1). Specifically, the extraction unit 12 inputs a three-month forecast of temperature acquired from the weather forecast data storage unit 10. Note that in step S1, the extraction unit 12 is not limited to inputting a forecast value acquired via the network NW, and may be configured to read and input a forecast value stored in a portable memory such as a USB memory.

[0077] Next, the learning unit 16 acquires the learning data from the learning data storage unit 17 and constructs the inference model 15a (step S20). Specifically, the learning unit 16 uses a known machine learning algorithm to learn the relationship between the temperature observed in the set second observation period and the actual measured value of the power demand during that period. For example, when the period to be predicted for the power demand is three months, the learning unit 16 can use learning data consisting of the observation data of the temperature for five months in the previous year and the actual measured value of the power demand for those five months. The inference model 15a constructed by the learning unit 16 is stored in the first storage unit 15.

[0078] Next, the inference unit 13 reads out the inference model 15a constructed in step S20 from the first storage unit 15, and obtains an inferred value of the power demand (step S2). More specifically, the inference unit 13 provides the observation data of the temperature for the past 30 years as unknown input to the inference model 15a, performs a calculation according to the inference model 15a, and obtains an inferred value of the power demand for the past 30 years.

[0079] Next, the extraction unit 12 uses the probability value of the long-term forecast of temperature input in step S1 to calculate an expected value for the inferred value of the power demand acquired in step S2 (step S3). Next, the extraction unit 12 obtains the power demand predicted in the forecast target period from the expected value calculated in step S3 (step S4). Specifically, the extraction unit 12 can calculate the expected value in step S3 according to the forecast period related to the long-term forecast. For example, if the long-term forecast is a three-month forecast every month, the extraction unit 12 can calculate the expected value in step S3 for each month's data and obtain the power demand predicted for up to three months ahead.

[0080] Next, the presentation unit 14 causes the display device 107 to present the power demand prediction result obtained in step S4 (step S5).

[0081] As described above, according to the power demand forecasting device 1A of the second embodiment, the learning unit 16 learns the relationship between past temperature observation data and past actual measured values ​​of power demand to construct the inference model 15a, so that the inference model 15a can be reconstructed each time the past temperature observation data is updated, enabling more accurate long-term forecasting of power demand.

[0082] In the embodiment described above, all the functional units are provided in one power demand prediction device 1. However, the functional units of the power demand prediction device 1 may be realized as one computer or may be distributed over a network.

[0083] In the embodiment described above, the power demand forecasting device 1 uses a three-month forecast of monthly temperatures as a long-term forecast. However, the long-term forecast is not limited to a three-month forecast, and a long-term forecast for other periods, such as a one-month forecast, may be used. The one-month forecast may further include a forecast for each week. For example, the one-month forecast may be forecast data having forecast values ​​for the first week, the second week, and the third and fourth weeks. In this case, the extraction unit 12 of the power demand forecasting device 1 can calculate expected values ​​for the first week, the second week, and the third and fourth weeks according to the forecast period for the long-term forecast, and obtain the predicted power demand.

[0084] The above describes the embodiments of the power demand forecasting device and power demand forecasting method of the present invention. However, the present invention is not limited to the described embodiments, and various modifications that may be envisioned by a person skilled in the art can be made within the scope of the invention described in the claims. [Explanation of symbols]

[0085] 1...power demand forecasting device, 10...weather forecast data memory unit, 11...weather data memory unit, 12...extraction unit, 13...inference unit, 14...presentation unit, 15...first memory unit, 15a...inference model, 101...bus, 102...processor, 103...main memory unit, 104...communication interface, 105...auxiliary memory unit, 106...input / output I / O, 107...display unit, NW...network.

Claims

1. an extracting unit for extracting a predicted electricity demand for a target period from an inferred value of an electricity demand for a first observation period among past observation periods based on a probabilistic weather forecast value compared with a normal value; A presentation unit that presents the extracted predicted power demand; Equipped with The inferred value is a value inferred for a first observation value, which is a meteorological observation value during the first observation period, based on a previously constructed inference model; The meteorological observation values ​​are temperature observation values ​​classified into a plurality of classes according to whether the values ​​are high or low relative to the normal value, which is an average value of temperatures during the past observation period; the forecast value of the probabilistic weather compared to the normal value is expressed as a probability that the distribution of temperature related to the forecast falls into each of the plurality of classes; The extraction unit extracts the predicted power demand by using an average value obtained by weighting the inferred value corresponding to a class that the first observation value in the first observation period can take with a probability of each of the multiple classes indicated by the forecast value. The power demand forecasting device according to the present invention is characterized in that:

2. - an extraction step of extracting a predicted electricity demand for a period of interest from an inferred value of the electricity demand for a first observation period of past observation periods based on a probabilistic weather forecast value compared to a normal value; A presentation step of presenting the extracted predicted power demand; Equipped with The inferred value is a value inferred for a first observation value, which is a meteorological observation value during the first observation period, based on a previously constructed inference model; The meteorological observation values ​​are temperature observation values ​​classified into a plurality of classes according to whether the values ​​are high or low relative to the normal value, which is an average value of temperatures during the past observation period; the forecast value of the probabilistic weather compared to the normal value is expressed as a probability that the distribution of temperature related to the forecast falls into each of the plurality of classes; The extraction step extracts the predicted power demand by using an average value of the inferred value corresponding to a class that the first observation value in the first observation period can take, weighted by a probability of each of the multiple classes indicated by the forecast value. A power demand forecasting method comprising:

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