WO2025079416A1 - Dispositif de prédiction et procédé de prédiction - Google Patents

Dispositif de prédiction et procédé de prédiction Download PDF

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Publication number
WO2025079416A1
WO2025079416A1 PCT/JP2024/033812 JP2024033812W WO2025079416A1 WO 2025079416 A1 WO2025079416 A1 WO 2025079416A1 JP 2024033812 W JP2024033812 W JP 2024033812W WO 2025079416 A1 WO2025079416 A1 WO 2025079416A1
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Prior art keywords
prediction
price
electricity
unit
model
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English (en)
Japanese (ja)
Inventor
恭弘 常行
剛 朝野
耕佑 原田
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Eneos Corp
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Eneos Corp
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Energy or water supply
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—ELECTRIC POWER NETWORKS; CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for AC mains or AC distribution networks

Definitions

  • Patent Document 1 discloses an electricity market price prediction device that predicts the electricity market price on a future prediction date based on the estimated supply of electricity generated by renewable energy on that prediction date.
  • the prediction device includes a verification prediction unit that predicts the electricity price during a specified verification period based on each of a plurality of trained prediction models, a selection unit that selects a prediction model having high prediction accuracy for the electricity price from among the plurality of prediction models, and a target prediction unit that predicts the electricity price during a specified target prediction period based on the selected prediction model.
  • the prediction device includes an acquisition unit that acquires a fuel price indicating the price of fuel used for power generation, and a prediction unit that predicts electricity prices by inputting explanatory variables including the fuel price into a trained prediction model.
  • the electricity price prediction system is capable of predicting any electricity price, so long as the electricity price is determined by a method based on legal grounds.
  • the electricity price determined by a method based on legal grounds does not mean that the electricity price is determined directly by law, but rather that the electricity price is determined by traders acting in accordance with certain rules established by law.
  • the electricity price may be, as an example, the electricity market price.
  • the electricity market price is the contract price at which an electricity transaction is concluded in the electricity trading market.
  • the electricity trading market may be a wholesale electricity trading market or a baseload market.
  • the wholesale electricity trading market may be a spot market, an hourly market, or a forward market.
  • the electricity trading market or wholesale electricity trading market is not limited to these, and may be any market in which electricity transactions are conducted.
  • the electricity price may be an imbalance charge unit price.
  • the imbalance charge unit price is a unit price at which a general electricity transmission and distribution company adjusts the surplus or shortage of electricity in actual supply and demand. In this embodiment, a configuration for predicting the electricity market price will be described as an example of the electricity price.
  • the present embodiment aims to accurately predict electricity market prices.
  • at least one of teacher data, prediction algorithms, and explanatory variables can be selected.
  • this embodiment can provide a prediction device that can accurately predict electricity market prices.
  • the external data source 30 is not limited to these and may include other data sources.
  • the external data source 30 may include other data sources that accumulate data obtained from the electricity market system 31, the LNG market system 32, the weather information system 33, or the foreign exchange market system 34, and process and provide the data.
  • the prediction device 10 is an information processing device such as a personal computer, workstation, or server that predicts the electricity market price formed in the wholesale electricity trading market.
  • the prediction device 10 acquires performance data from an external data source 30, and generates multiple prediction models based on the performance data.
  • the prediction device 10 selects a prediction model with high prediction accuracy from the multiple prediction models, and predicts the electricity market price using the selected prediction model.
  • the prediction device 10 transmits the predicted result of the electricity market price to the terminal device 20.
  • the terminal device 20 is an information processing terminal such as a personal computer, smartphone, or tablet terminal operated by a user of the electricity price prediction system 1000.
  • the terminal device 20 receives the prediction results of the electricity market price from the prediction device 10 and presents the prediction results to the user.
  • the electricity market system 31 is an information processing system that provides a wholesale electricity trading market.
  • the wholesale electricity trading market is a market where electricity trading takes place between retail electricity suppliers and power generation suppliers.
  • One example of a wholesale electricity trading market is the Japan Electric Power Exchange (JEPX).
  • JEPX Japan Electric Power Exchange
  • multiple electricity market prices may be formed, for example, by region or by power generation method.
  • the electricity market system 31 accumulates actual values of electricity market prices formed in the wholesale electricity trading market.
  • the LNG market system 32 is an information processing system that provides an LNG market.
  • the LNG market is a market where LNG futures trading takes place.
  • An example of an LNG market is the Tokyo Commodity Exchange.
  • the LNG market system 32 accumulates actual values of index prices that are statistically processed from futures prices formed in the LNG market.
  • An example of an index price is Platts JKM (Japan Korea Marker).
  • the foreign exchange market system 34 is an information processing system that provides a foreign exchange market.
  • the foreign exchange market is a market where different currencies are exchanged.
  • An example of a foreign exchange market is the Tokyo Foreign Exchange Market.
  • the foreign exchange market system 34 accumulates the actual values of exchange rates formed in the foreign exchange market.
  • An example of an exchange rate is the US dollar/Japanese yen rate.
  • the prediction device 10 and the terminal device 20 in the first embodiment are realized by, for example, a computer.
  • Fig. 2 is a block diagram showing an example of the hardware configuration of a computer.
  • the computer 500 includes a CPU (Central Processing Unit).
  • the computer 500 has a CPU 501, a read only memory (ROM) 502, a random access memory (RAM) 503, a hard disk drive (HDD) 504, an input device 505, a display device 506, a communication I/F (Interface) 507, and an external I/F 508.
  • the CPU 501, the ROM 502, and the RAM 503 form a so-called computer.
  • Each piece of hardware of the computer 500 is connected to each other via a bus line 509.
  • the input device 505 and the display device 506 may be connected to the external I/F 508 for use.
  • the RAM 503 is an example of a volatile semiconductor memory (storage device) from which programs and data are erased when the power is turned off.
  • the RAM 503 may be, for example, a dynamic random access memory (DRAM) or a static random access memory (SRAM).
  • DRAM dynamic random access memory
  • SRAM static random access memory
  • the RAM 503 provides a working area in which various programs installed in the HDD 504 are expanded when the CPU 501 executes them.
  • HDD 504 is an example of a non-volatile storage device that stores programs and data.
  • the programs and data stored in HDD 504 include the OS, which is the basic software that controls the entire computer 500, and applications that provide various functions on the OS.
  • computer 500 may use a storage device that uses flash memory as a storage medium (e.g., SSD: Solid State Drive, etc.).
  • the performance data acquisition unit 101, teacher data generation unit 103, model learning unit 104, verification prediction unit 106, accuracy calculation unit 107, model selection unit 108, and target prediction unit 109 are realized, for example, by a process executed by the CPU 501 of a program loaded onto the RAM 503 from the HDD 504 shown in FIG. 2.
  • the data storage unit 102 and the model storage unit 105 are realized, for example, using the RAM 503 or HDD 504 shown in FIG. 2.
  • the training data includes some or all of the following data items. However, at least the electricity market price is included in the training data.
  • the electricity market price is used as the objective variable.
  • the other data items are used as explanatory variables.
  • the LNG market index price is an example of a fuel price.
  • the fuel price is the price of the fuel used for power generation. Specifically, the fuel price may be the price of fuel used in a power plant located in the prediction target area for predicting the electricity market price.
  • ⁇ Electricity market price ⁇ LNG market index price ⁇ Exchange rate ⁇ Calendar feature value ⁇ Weather forecast data ⁇ Solar power generation forecast ⁇ Electricity demand forecast ⁇ Lag feature value of electricity market price ⁇ Statistics of electricity market price
  • the calendar features may include, for example, the day of the week, time, holiday information, etc.
  • the weather forecast data may include, for example, a 39-hour forecast or a 78-hour forecast of the Meso Scale Model - Grid Point Value (MSM-GPV) provided by the Japan Meteorological Agency.
  • MSM-GPV Meso Scale Model - Grid Point Value
  • the lag feature of the electricity market price may be generated by extracting the actual value of the electricity market price during the learning period from the electricity market price data, and shifting the extracted electricity market price by a predetermined time interval.
  • the electricity market price statistics may be, for example, a moving average or a moving standard deviation.
  • the moving average or moving standard deviation may be generated by extracting the actual value of the electricity market price during the learning period from the electricity market price data, and calculating the average or standard deviation for each of a predetermined number of electricity market prices from the extracted electricity market prices.
  • the prediction algorithm includes at least one of a machine learning model and a statistical model.
  • the machine learning model include Random Forest, LightGBM (Light Gradient Boosting Machine), or XGBoost (eXtreme
  • the statistical model may be, for example, a linear regression or an autoregressive model.
  • the autoregressive model may be, for example, an autoregressive integrated moving average (ARIMA) model.
  • the prediction algorithm may include multiple types of machine learning models or multiple types of statistical models.
  • the model learning unit 104 may generate multiple prediction models with different explanatory variables for each combination of teacher data and prediction algorithm.
  • the explanatory variables may be predetermined as explanatory variables that can be used depending on the prediction algorithm.
  • the multiple prediction models include prediction models that correspond to each combination of teacher data, prediction algorithm, and explanatory variables.
  • the verification prediction unit 106 predicts the electricity market price in a specified verification period based on each of the multiple prediction models read from the model storage unit 105.
  • the verification period may be the time period immediately preceding the prediction period.
  • the prediction period is one or more time periods for which the electricity market price is predicted.
  • the prediction period may be the next day, or several days after the next day, as an example. For example, if the prediction period is the next day, the verification period may be the time period before the previous day (if the electricity market price on the prediction day has not been made public).
  • the length of the verification period may be determined arbitrarily, but may be, for example, several days to about a week.
  • the performance data acquisition unit 101 also acquires unacquired LNG price data from the LNG market system 32. Next, the performance data acquisition unit 101 adds the acquired LNG price data to the LNG price data stored in the data storage unit 102.
  • the performance data acquisition unit 101 acquires exchange rate data from the exchange market system 34. Next, the performance data acquisition unit 101 adds the acquired exchange rate data to the exchange rate data stored in the data storage unit 102.
  • step S2 the teacher data generation unit 103 of the prediction device 10 reads out the performance data stored in the data storage unit 102.
  • the teacher data generation unit 103 generates multiple pieces of teacher data based on the read out performance data.
  • the teacher data generation unit 103 sends the multiple pieces of teacher data that have been generated to the model learning unit 104.
  • the teacher data generation unit 103 may generate multiple teacher data sets with different explanatory variables based on multiple teacher data sets with different learning periods. In this case, the teacher data generation unit 103 may extract explanatory variables included in a predetermined combination of explanatory variables from each of the multiple teacher data sets with different learning periods.
  • the verification prediction unit 106 inputs explanatory variables including the extracted performance data into each of a plurality of prediction models. Each prediction model predicts the electricity market price based on the input explanatory variables and outputs the predicted value. The verification prediction unit 106 then sends the predicted value of the electricity market price output from each prediction model to the accuracy calculation unit 107.
  • step S5 the accuracy calculation unit 107 of the prediction device 10 receives the predicted value of the electricity market price from the verification prediction unit 106.
  • the accuracy calculation unit 107 calculates the prediction accuracy for each of the multiple prediction models based on the predicted value of the electricity market price and the actual value of the electricity market price. Specifically, the accuracy calculation unit 107 calculates the mean absolute error between the predicted value of the electricity market price and the actual value of the electricity market price. Then, the accuracy calculation unit 107 sends the prediction accuracy of each prediction model to the model selection unit 108.
  • step S6 the model selection unit 108 of the prediction device 10 receives the prediction accuracy of each prediction model from the accuracy calculation unit 107.
  • the model selection unit 108 selects a prediction model with high prediction accuracy based on the prediction accuracy of each prediction model. Specifically, the model selection unit 108 selects one or more prediction models with smallest mean absolute error. Then, the model selection unit 108 sends information indicating the selected prediction model (hereinafter also referred to as "selection information") to the target prediction unit 109.
  • selection information may be, for example, identification information assigned to each prediction model.
  • the target prediction unit 109 then extracts the most recently generated actual data from the actual data read in step S2. Next, the target prediction unit 109 inputs explanatory variables including the extracted actual data into the prediction model selected in step S6. The prediction model predicts the electricity market price based on the input explanatory variables and outputs the predicted value. The target prediction unit 109 then obtains the predicted value of the electricity market price output from the prediction model.
  • step S8 the target prediction unit 109 of the prediction device 10 transmits the prediction result of the electricity market price to the terminal device 20.
  • the prediction result of the electricity market price includes the predicted value of the electricity market price acquired in step S7.
  • the prediction result of the electricity market price may also include the predicted values of the electricity market price predicted in the past in a time series.
  • the terminal device 20 receives the prediction result of the electricity market price from the prediction device 10. Next, the terminal device 20 presents the prediction result of the electricity market price to the user. For example, the terminal device 20 may display the prediction result of the electricity market price on the display device 506. The terminal device 20 may display the prediction result of the electricity market price in any display format. For example, the terminal device 20 may display the trend of the electricity market price in a graph.
  • the prediction device 10 repeatedly executes the processes from step S1 to step S6 and the processes from step S7 to step S8 at a predetermined time interval.
  • the predetermined time interval may be set arbitrarily, and may be, for example, within one week.
  • the time interval for executing the processes from step S1 to step S6 and the time interval for executing the processes from step S7 to step S8 may be the same or different.
  • the prediction device 10 repeatedly generates a prediction model and selects a prediction model at a predetermined time interval. This allows the prediction device 10 to predict the electricity market price based on a prediction model with high prediction accuracy among prediction models updated based on the most recent performance data.
  • the prediction device 10 in the first embodiment selects a prediction model having high prediction accuracy for the electricity market price in a verification period from among a plurality of trained prediction models, and predicts the electricity market price in a prediction target period based on the selected prediction model. In one aspect, according to the first embodiment, it is possible to predict the electricity price with high accuracy.
  • the multiple prediction models may differ from each other in the period in which the training data was generated.
  • the multiple prediction models may differ from each other in prediction algorithms.
  • the multiple prediction models may include at least one of a machine learning model or a statistical model.
  • the multiple prediction models may differ from each other in explanatory variables.
  • the explanatory variables may include an LNG market index price. Therefore, according to the first embodiment, it is possible to select a prediction model with high prediction accuracy from multiple prediction models having different training data, prediction algorithms, or explanatory variables.
  • the prediction device 10 may repeatedly generate a prediction model and select a prediction model at a predetermined time interval. Because factors that affect electricity market prices change from moment to moment, it is believed that prediction accuracy can be improved by updating the prediction model at short cycles. Therefore, according to this embodiment, the prediction model can be appropriately updated based on the most recent performance data.
  • a second embodiment of the present invention is an electricity price prediction system for predicting an electricity price.
  • the electricity price prediction system in the second embodiment has a function of predicting an electricity price using the price of fuel used for power generation.
  • a configuration for predicting an electricity market price as an example of an electricity price will be described.
  • a configuration is described in which the most recent fuel price is used to predict the electricity market price.
  • a configuration is described in which a past fuel price is used to predict the electricity market price.
  • a configuration is described in which the index price of the LNG market is used as an example of the fuel price, but the fuel price may be the price of other fuels used for power generation.
  • the prediction device 10 includes a performance data acquisition unit 101, a data storage unit 102, a teacher data generation unit 103, a model learning unit 104, a model storage unit 105, a target prediction unit 109, and a time length determination unit 110. That is, the prediction device 10 in the second embodiment differs from the first embodiment in that it does not include the verification prediction unit 106, the accuracy calculation unit 107, and the model selection unit 108, but further includes the time length determination unit 110.
  • Fig. 6 is a flowchart showing an example of the prediction method in the second embodiment.
  • step S13 the model learning unit 104 of the prediction device 10 receives the teacher data from the teacher data generation unit 103.
  • the model learning unit 104 learns the teacher data based on a predetermined prediction algorithm.
  • the model learning unit 104 updates the prediction model stored in the model storage unit 105 with the generated prediction model.
  • Table 1 shows the results of calculating the mean absolute error for forecast values using the LNG price of the day and the LNG price of seven days prior. As shown in Table 1, the mean absolute error for forecast values using the LNG price of the day is smaller than that for forecast values using the LNG price of seven days prior, indicating that the forecast accuracy is higher.
  • the prediction device 10 may determine the time length based on the correlation coefficient between the change in the electricity market price and the change in the fuel price. It is believed that the electricity market price can be predicted more accurately by using the fuel price from a period when the fuel price has a high correlation with the electricity market price among past fuel prices. Therefore, according to the second embodiment, the electricity price can be predicted with high accuracy.
  • a prediction device comprising: (Appendix 10) The fuel price is a liquefied natural gas market index price; 10. The prediction device of claim 9. (Appendix 11) The fuel price is a price at a time point a predetermined time period prior to a forecast period for predicting the electricity price. 10. The prediction device of claim 9. (Appendix 12) The fuel price is a moving average price for a predetermined time period prior to a forecast period for predicting the electricity price. 10. The prediction device of claim 9.
  • (Appendix 13) a time length determining unit that determines the time length based on a correlation coefficient between the change in the electricity price and the change in the fuel price; 13.
  • (Appendix 14) A model learning unit that generates the prediction model, generating the prediction model by the model learning unit; determining the time length by the time length determining unit; Repeatedly execute at a predetermined time interval. 14. The prediction device of claim 13.
  • the computer A step of predicting electricity prices for a predetermined validation period based on each of a plurality of trained prediction models; selecting a prediction model having high prediction accuracy of the electricity price from among the plurality of prediction models; predicting the electricity price for a predetermined prediction period based on the selected prediction model; A forecasting method to perform.
  • Prediction device 20 Terminal device 31: Electricity market system 32: LNG market system 33: Weather information system 34: Foreign exchange market system 101: Performance data acquisition unit 102: Data storage unit 103: Teacher data generation unit 104: Model learning unit 105: Model storage unit 106: Verification prediction unit 107: Accuracy calculation unit 108: Model selection unit 109: Target prediction unit 110: Time length determination unit 1000: Electricity price prediction system

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Abstract

La présente invention porte sur un dispositif de prédiction qui comprend : une unité de prédiction de vérification qui prédit des prix d'électricité dans une période de vérification prédéterminée, sur la base de chacun d'une pluralité de modèles de prédiction entraînés ; une unité de sélection de modèle qui sélectionne un modèle de prédiction ayant une précision élevée pour prédire des prix d'électricité parmi les modèles de prédiction ; et une unité de prédiction cible qui prédit des prix d'électricité dans une période cible de prédiction, sur la base du modèle de prédiction sélectionné.
PCT/JP2024/033812 2023-10-11 2024-09-24 Dispositif de prédiction et procédé de prédiction Pending WO2025079416A1 (fr)

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Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2018092439A (ja) * 2016-12-05 2018-06-14 株式会社日立製作所 データ処理システム及びデータ処理方法
WO2021210107A1 (fr) * 2020-04-15 2021-10-21 日本電信電話株式会社 Dispositif de création de modèle, procédé de création de modèle, et programme de création de modèle
JP2022002063A (ja) * 2020-06-22 2022-01-06 株式会社日立製作所 予測システムおよび予測方法
CN114372822A (zh) * 2021-12-28 2022-04-19 大连理工大学 一种模型-数据混合驱动的双边电力市场电价预测方法

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2018092439A (ja) * 2016-12-05 2018-06-14 株式会社日立製作所 データ処理システム及びデータ処理方法
WO2021210107A1 (fr) * 2020-04-15 2021-10-21 日本電信電話株式会社 Dispositif de création de modèle, procédé de création de modèle, et programme de création de modèle
JP2022002063A (ja) * 2020-06-22 2022-01-06 株式会社日立製作所 予測システムおよび予測方法
CN114372822A (zh) * 2021-12-28 2022-04-19 大连理工大学 一种模型-数据混合驱动的双边电力市场电价预测方法

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