电源技术2025，Vol.49Issue(4)：869-882,14.DOI:10.3969/j.issn.1002-087X.2025.04.025
计及CCM和改进GRA的PSO-BiLSTM光伏出力预测模型
PSO-BiLSTM PV output prediction model with CCM and improved GRA
摘要
Abstract
To significantly enhance the prediction accuracy of the output power of photovoltaic(PV)power station,this paper developed an intelligent prediction model for PV output through incorporat-ing CCM,IGRA,PSO and BiLSTM into a general framework.Firstly,the convergent cross mapping(CCM)algorithm was employed to extract key meteorological elements affecting PV output,where they are considered as major evaluation criteria of similar day selection and critical input variables of subsequently established prediction model;secondly,an improved grey relational analysis method(IGRA)based on entropy weight method was utilized to select historical similar days that closely match meteorological characteristics of the day to be predicted.And then,selecting the critical weather parameters and PV power generation sequence of similar days as the training samples,the particle swarm optimization(PSO)algorithm was used to determine optimal hyperparameters combi-nation for the bidirectional long short-term memory(Bi-LSTM)network.A high-precision PV output prediction model based on CCM-IGRA-PSO-BiLSTM for the predicted days was established.Three criteria,including mean absolute error(MAE),mean absolute percentage error(MAPE)and root mean square error(RMSE),were selected as the evaluation metrics for model performance.The ob-tained results indicate that,taking the sunny weather in spring as an example,the proposed com-bined model achieved MAPE,MAE and RMSE of 0.38%,0.06 and 0.07 MW,respectively,all of which surpass those of several baseline models.This way provides scientific guidance and support for the station to formulate reasonable production plan and electricity market participation strategy.关键词
光伏出力预测/粒子群优化/收敛交叉映射/改进的灰色关联分析法/双向长短期记忆网络Key words
photovoltaic output prediction/particle swarm optimization/convergent cross mapping/improved grey relation analysis method/bidirectional long short-term memory network分类
信息技术与安全科学引用本文复制引用
高胜强,张琳,王海鹏,宋煜,燕灏,刘紫凝,周维维,卜帅羽..计及CCM和改进GRA的PSO-BiLSTM光伏出力预测模型[J].电源技术,2025,49(4):869-882,14.基金项目
国网北京市电力公司科技项目资助(520206240001) （520206240001）
