DR potential probabilistic forecasting model of load aggregators based on ensemble learning
Day-ahead forecasting on the demand response (DR) potential of the load aggregators could provide important reference information for the quotations and volumes of load aggregators in the electricity market, thus reducing decision-making risks. Aiming at the shortcomings of generalization and reliab...
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| Format: | Article |
| Language: | zho |
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Harbin Jinhe Electrical Measurement & Instrumentation Magazine Publishing Co., Ltd.
2025-04-01
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| Series: | Diance yu yibiao |
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| Online Access: | http://www.emijournal.net/dcyyben/ch/reader/create_pdf.aspx?file_no=20220713013&flag=1&journal_id=dcyyben&year_id=2025 |
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| author | YEERSEN Sailike YANG Xi LI Meiyi LI Na GE Xinxin WANG Fei |
| author_facet | YEERSEN Sailike YANG Xi LI Meiyi LI Na GE Xinxin WANG Fei |
| author_sort | YEERSEN Sailike |
| collection | DOAJ |
| description | Day-ahead forecasting on the demand response (DR) potential of the load aggregators could provide important reference information for the quotations and volumes of load aggregators in the electricity market, thus reducing decision-making risks. Aiming at the shortcomings of generalization and reliability of a single-point forecasting model, this paper proposes an online DR potential probabilistic forecasting model of load aggregators based on ensemble learning, which can effectively improve the accuracy and generalization ability of the probabilistic forecasting model. Firstly, the multivariate influencing features of the DR potential of load aggregators are extracted, and the support vector machine-based recursive feature elimination (SVM-RFE) method is used to select features. Secondly, multiple single probabilistic forecasting models are proposed based on the non-parametric kernel density estimation. Finally, a DR potential ensemble probabilistic forecasting model of load aggregators based on "repeated game, dynamic update" is established, which adaptively learns the weights of each base model through the idea of game theory and dynamically update the weights over time. Simulation experiments show that the probabilistic forecasting model proposed in this paper has better accuracy and generalization than a single prediction model. |
| format | Article |
| id | doaj-art-e2e2eadd353d4b47b0c1b5023ff5fffb |
| institution | DOAJ |
| issn | 1001-1390 |
| language | zho |
| publishDate | 2025-04-01 |
| publisher | Harbin Jinhe Electrical Measurement & Instrumentation Magazine Publishing Co., Ltd. |
| record_format | Article |
| series | Diance yu yibiao |
| spelling | doaj-art-e2e2eadd353d4b47b0c1b5023ff5fffb2025-08-20T02:57:55ZzhoHarbin Jinhe Electrical Measurement & Instrumentation Magazine Publishing Co., Ltd.Diance yu yibiao1001-13902025-04-01624889610.19753/j.issn1001-1390.2025.04.0111001-1390(2025)04-0088-09DR potential probabilistic forecasting model of load aggregators based on ensemble learningYEERSEN Sailike0YANG Xi1LI Meiyi2LI Na3GE Xinxin4WANG Fei5Marketing Service Center (Capital Intensive Center, Measurement Center), State Grid Xinjiang Electric Power Co., Ltd., Urumqi 830001, ChinaMarketing Service Center (Capital Intensive Center, Measurement Center), State Grid Xinjiang Electric Power Co., Ltd., Urumqi 830001, ChinaNorth China Electric Power University, Baoding 071003, Hebei, ChinaMarketing Service Center (Capital Intensive Center, Measurement Center), State Grid Xinjiang Electric Power Co., Ltd., Urumqi 830001, ChinaNorth China Electric Power University, Baoding 071003, Hebei, ChinaNorth China Electric Power University, Baoding 071003, Hebei, ChinaDay-ahead forecasting on the demand response (DR) potential of the load aggregators could provide important reference information for the quotations and volumes of load aggregators in the electricity market, thus reducing decision-making risks. Aiming at the shortcomings of generalization and reliability of a single-point forecasting model, this paper proposes an online DR potential probabilistic forecasting model of load aggregators based on ensemble learning, which can effectively improve the accuracy and generalization ability of the probabilistic forecasting model. Firstly, the multivariate influencing features of the DR potential of load aggregators are extracted, and the support vector machine-based recursive feature elimination (SVM-RFE) method is used to select features. Secondly, multiple single probabilistic forecasting models are proposed based on the non-parametric kernel density estimation. Finally, a DR potential ensemble probabilistic forecasting model of load aggregators based on "repeated game, dynamic update" is established, which adaptively learns the weights of each base model through the idea of game theory and dynamically update the weights over time. Simulation experiments show that the probabilistic forecasting model proposed in this paper has better accuracy and generalization than a single prediction model.http://www.emijournal.net/dcyyben/ch/reader/create_pdf.aspx?file_no=20220713013&flag=1&journal_id=dcyyben&year_id=2025load aggregatordr potentialensemble learningprobabilistic forecasting |
| spellingShingle | YEERSEN Sailike YANG Xi LI Meiyi LI Na GE Xinxin WANG Fei DR potential probabilistic forecasting model of load aggregators based on ensemble learning Diance yu yibiao load aggregator dr potential ensemble learning probabilistic forecasting |
| title | DR potential probabilistic forecasting model of load aggregators based on ensemble learning |
| title_full | DR potential probabilistic forecasting model of load aggregators based on ensemble learning |
| title_fullStr | DR potential probabilistic forecasting model of load aggregators based on ensemble learning |
| title_full_unstemmed | DR potential probabilistic forecasting model of load aggregators based on ensemble learning |
| title_short | DR potential probabilistic forecasting model of load aggregators based on ensemble learning |
| title_sort | dr potential probabilistic forecasting model of load aggregators based on ensemble learning |
| topic | load aggregator dr potential ensemble learning probabilistic forecasting |
| url | http://www.emijournal.net/dcyyben/ch/reader/create_pdf.aspx?file_no=20220713013&flag=1&journal_id=dcyyben&year_id=2025 |
| work_keys_str_mv | AT yeersensailike drpotentialprobabilisticforecastingmodelofloadaggregatorsbasedonensemblelearning AT yangxi drpotentialprobabilisticforecastingmodelofloadaggregatorsbasedonensemblelearning AT limeiyi drpotentialprobabilisticforecastingmodelofloadaggregatorsbasedonensemblelearning AT lina drpotentialprobabilisticforecastingmodelofloadaggregatorsbasedonensemblelearning AT gexinxin drpotentialprobabilisticforecastingmodelofloadaggregatorsbasedonensemblelearning AT wangfei drpotentialprobabilisticforecastingmodelofloadaggregatorsbasedonensemblelearning |