Deep learning for day-ahead electricity price forecasting
Deregulation exposes the inherent volatility of the electricity price. Accurate electricity price forecasting (EPF) could help the market participants to hedge against the price movements and maximise their profits. The existing methods have limited capability of integrating other external factors i...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-02-01
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| Series: | IET Smart Grid |
| Subjects: | |
| Online Access: | https://digital-library.theiet.org/content/journals/10.1049/iet-stg.2019.0258 |
| _version_ | 1818967335853096960 |
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| author | Chi Zhang Ran Li Ran Li Heng Shi Furong Li |
| author_facet | Chi Zhang Ran Li Ran Li Heng Shi Furong Li |
| author_sort | Chi Zhang |
| collection | DOAJ |
| description | Deregulation exposes the inherent volatility of the electricity price. Accurate electricity price forecasting (EPF) could help the market participants to hedge against the price movements and maximise their profits. The existing methods have limited capability of integrating other external factors into the forecasting model, such as weather, electricity consumption and natural gas price. This study proposes a deep recurrent neural network (DRNN) method to forecast day-ahead electricity price in a deregulated electricity market to explore the complex dependence structure of the multivariate EPF model. The proposed method can learn the indirect relationship between electricity price and external factors through its efficient diverse function and multi-layer structure. The effectiveness of the method is validated using data from the New England electricity market. Compared with the up-to-date techniques, the proposed DRNN outperforms the single support vector machine (SVM) by 29.71%, and the improved hybrid SVM network by 21.04% in terms of mean absolute percentage error. |
| first_indexed | 2024-12-20T13:47:10Z |
| format | Article |
| id | doaj.art-bf539352b98048d396f126e8eca14f17 |
| institution | Directory Open Access Journal |
| issn | 2515-2947 |
| language | English |
| last_indexed | 2024-12-20T13:47:10Z |
| publishDate | 2020-02-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Smart Grid |
| spelling | doaj.art-bf539352b98048d396f126e8eca14f172022-12-21T19:38:37ZengWileyIET Smart Grid2515-29472020-02-0110.1049/iet-stg.2019.0258IET-STG.2019.0258Deep learning for day-ahead electricity price forecastingChi Zhang0Ran Li1Ran Li2Heng Shi3Furong Li4University of BathUniversity of BathUniversity of BathEnflame CompanyUniversity of BathDeregulation exposes the inherent volatility of the electricity price. Accurate electricity price forecasting (EPF) could help the market participants to hedge against the price movements and maximise their profits. The existing methods have limited capability of integrating other external factors into the forecasting model, such as weather, electricity consumption and natural gas price. This study proposes a deep recurrent neural network (DRNN) method to forecast day-ahead electricity price in a deregulated electricity market to explore the complex dependence structure of the multivariate EPF model. The proposed method can learn the indirect relationship between electricity price and external factors through its efficient diverse function and multi-layer structure. The effectiveness of the method is validated using data from the New England electricity market. Compared with the up-to-date techniques, the proposed DRNN outperforms the single support vector machine (SVM) by 29.71%, and the improved hybrid SVM network by 21.04% in terms of mean absolute percentage error.https://digital-library.theiet.org/content/journals/10.1049/iet-stg.2019.0258learning (artificial intelligence)power system economicspower engineering computingeconomic forecastingpricingsupport vector machinesrecurrent neural netspower marketsderegulated electricity marketmultivariate epf modelnew england electricity marketdeep learningday-ahead electricity price forecastingaccurate electricity price forecastingmarket participantsprice movementsforecasting modelelectricity consumptionnatural gas pricedeep recurrent neural network method |
| spellingShingle | Chi Zhang Ran Li Ran Li Heng Shi Furong Li Deep learning for day-ahead electricity price forecasting IET Smart Grid learning (artificial intelligence) power system economics power engineering computing economic forecasting pricing support vector machines recurrent neural nets power markets deregulated electricity market multivariate epf model new england electricity market deep learning day-ahead electricity price forecasting accurate electricity price forecasting market participants price movements forecasting model electricity consumption natural gas price deep recurrent neural network method |
| title | Deep learning for day-ahead electricity price forecasting |
| title_full | Deep learning for day-ahead electricity price forecasting |
| title_fullStr | Deep learning for day-ahead electricity price forecasting |
| title_full_unstemmed | Deep learning for day-ahead electricity price forecasting |
| title_short | Deep learning for day-ahead electricity price forecasting |
| title_sort | deep learning for day ahead electricity price forecasting |
| topic | learning (artificial intelligence) power system economics power engineering computing economic forecasting pricing support vector machines recurrent neural nets power markets deregulated electricity market multivariate epf model new england electricity market deep learning day-ahead electricity price forecasting accurate electricity price forecasting market participants price movements forecasting model electricity consumption natural gas price deep recurrent neural network method |
| url | https://digital-library.theiet.org/content/journals/10.1049/iet-stg.2019.0258 |
| work_keys_str_mv | AT chizhang deeplearningfordayaheadelectricitypriceforecasting AT ranli deeplearningfordayaheadelectricitypriceforecasting AT ranli deeplearningfordayaheadelectricitypriceforecasting AT hengshi deeplearningfordayaheadelectricitypriceforecasting AT furongli deeplearningfordayaheadelectricitypriceforecasting |