现代电力2025，Vol.42Issue(2)：360-368,9.DOI:10.19725/j.cnki.1007-2322.2023.0017
基于集合经验模态分解和Q学习策略的短期负荷预测模型
Short-term Load Forecasting Model Based on Ensemble Empirical Mode Decomposition and Q Learning Strategy
摘要
Abstract
Short-term load forecasting is of great significance to the safe and stable operation of power systems.For that reas-on,a short-term load forecasting model based on ensemble em-pirical mode decomposition(abbr.EEMD)and Q learning strategy optimization was proposed.Firstly,the original load series was decomposed by EEMD to reduce the difficulty of forecasting.Secondly,on this basis,four deep learning models,namely,convolution neural network(abbr.CNN),residual neural network(abbr.ResNet),long short-term memory(abbr.LSTM)neural network and gated recurrent unit(abbr.GRU)were respectively used for forecasting to obtain four forecast-ing results,of which weighted combination was used to obtain the final load forecasting value.Thirdly,the combination weight was optimized by Q learning algorithm to maximize the forecasting performance of the combination model.Finally,the experiment was conducted using real collected load data from a certain region,and the results showed that the proposed com-bined forecasting model is superior to other forecasting models,and the effectiveness of the proposed model was verified.关键词
短期负荷预测/集合经验模态分解/深度学习模型/Q学习策略Key words
short-term load forecasting/ensemble empirical mode decomposition/deep learning model/Q learning strategy分类
动力与电气工程引用本文复制引用
段秦尉,何祥针,潮铸,谢祥中,兰萱丽..基于集合经验模态分解和Q学习策略的短期负荷预测模型[J].现代电力,2025,42(2):360-368,9.基金项目
中国南方电网有限责任公司科技项目036000KK52210065(GDKJXM20210096).Project Supported by Science and Technology Research and Development Project of China Southern Power Grid Co.,Ltd.036000KK52210065(GDKJXM20210096). （GDKJXM20210096）
