电力系统保护与控制2024，Vol.52Issue(9)：143-153,11.DOI:10.19783/j.cnki.pspc.231357
基于改进Q学习算法和组合模型的超短期电力负荷预测
Ultra-short-term power load forecasting based on an improved Q-learning algorithm and combination model
摘要
Abstract
The prediction accuracy of a single model will deteriorate because of load fluctuations when making ultra-short-term load forecasting.To solve this problem,this paper proposes a combinatorial prediction model based on a deep learning algorithm.First,variational mode decomposition is used to decompose the original load sequence to obtain a series of sub-sequences.Secondly,a bidirectional long short-term memory network and an optimized deep extreme learning machine are used to predict each sub-sequence.Thirdly,the improved Q-learning algorithm is used to weight the prediction results of the bidirectional long short-term memory network and of the deep extreme learning machine to obtain those of each sub-sequence.Finally,the prediction results of each subseries are summed to obtain the final load prediction results.The results show that the prediction model proposed in this paper performs better than other models in ultra-short-term load forecasting,with a prediction accuracy of more than 98%.关键词
Q学习算法/负荷预测/双向长短期记忆/深度极限学习机/灰狼算法Key words
Q-learning algorithm/load forecasting/bi-directional long short-term memory/deep extreme learning machine/grey wolf optimization algorithm引用本文复制引用
张丽,李世情,艾恒涛,张涛,张宏伟..基于改进Q学习算法和组合模型的超短期电力负荷预测[J].电力系统保护与控制,2024,52(9):143-153,11.基金项目
This work is supported by the National Natural Science Foundation of China(No.52177039). 国家自然科学基金项目资助(52177039) （No.52177039）
河南省高等学校重点科研项目资助(24A470006) （24A470006）
河南省科技攻关项目(242102241027) （242102241027）
