基于集成学习的负荷聚合商需求响应潜力概率预测模型

叶尔森.赛里克; 杨希; 李美颐; 李娜; 葛鑫鑫; 王飞

叶尔森.赛里克,杨希,李美颐,李娜,葛鑫鑫,王飞.基于集成学习的负荷聚合商需求响应潜力概率预测模型[J].电测与仪表,2025,62(4):88-96.
YEERSEN Sailike,YANG Xi,LI Meiyi,LI Na,GE Xinxin,WANG Fei.DR potential probabilistic forecasting model of load aggregators based on ensemble learning[J].Electrical Measurement & Instrumentation,2025,62(4):88-96.

基于集成学习的负荷聚合商需求响应潜力概率预测模型

DR potential probabilistic forecasting model of load aggregators based on ensemble learning

DOI：10.19753/j.issn1001-1390.2025.04.011

中文关键词: 负荷聚合商需求响应潜力集成学习概率预测

英文关键词:load aggregator, DR potential, ensemble learning, probabilistic forecasting

基金项目:国网新疆电力有限公司科技项目(SGXJYX00XXJS2200051)，国家重点研发计划政府间国际科技创新合作重点专项(2018YFE0122200)

作者中文名	作者英文名	单位
叶尔森.赛里克	YEERSEN Sailike	国网新疆电力有限公司营销服务中心（资金集约中心、计量中心）；智慧用能及能效服务数字物理混合仿真实验室
杨希	YANG Xi	国网新疆电力有限公司营销服务中心（资金集约中心、计量中心）；智慧用能及能效服务数字物理混合仿真实验室
李美颐	LI Meiyi	华北电力大学
李娜	LI Na	国网新疆电力有限公司营销服务中心（资金集约中心、计量中心）;智慧用能及能效服务数字物理混合仿真实验室
葛鑫鑫	GE Xinxin	华北电力大学
王飞	WANG Fei	华北电力大学

摘要点击次数: 187

中文摘要:

对负荷聚合商的需求响应(demand response, DR)潜力进行日前预测可为负荷聚合商在电力市场中的投标报价提供重要参考信息，降低其市场交易风险。针对单一点预测模型在可靠性和泛化性方面的不足，文中提出了一种基于集成学习的负荷聚合商日前DR潜力概率预测模型，可有效提高概率预测模型的精度和泛化能力。首先提取影响负荷聚合商DR潜力的多元特征，并采用基于支持向量机的递归特征消除法(support vector machine recursive feature elimination, SVM-RFE)筛选特征；其次，基于非参数核密度估计分别建立多个单一概率预测模型；最后建立“重复博弈，动态更新”的负荷聚合商DR潜力集成概率预测模型，该模型通过重复博弈自适应学习每个基模型的权重，并随着时间的推移动态更新。仿真实验表明文中所提概率预测模型相较单一预测模型具有更好的预测精度和泛化性。

英文摘要:

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.

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