电气技术2024，Vol.25Issue(5)：31-40,10.
基于增量学习的CNN-LSTM光伏功率预测
CNN-LSTM photovoltaic power prediction based on incremental learning
摘要
Abstract
Most photovoltaic(PV)power prediction models adopt batch offline training,which poses a challenge on dealing with limited training data for newly established PV power plants.In order to address this issue,a PV power prediction model based on a combination of convolutional neural network(CNN)and long short-term memory(LSTM)network using incremental learning is proposed.Firstly,the CNN is used to extract the features of the meteorological data,and the power prediction is carried out through the LSTM network.The CNN-LSTM hybrid model is used for background learning,to train a baseline model that can be used for incremental learning.Secondly,incremental learning training is carried out according to different time spans to realize the online update of the model.In order to solve the problem of catastrophic forgetting in incremental learning,this paper uses the elastic weight consolidation(EWC)algorithm and the online elastic consolidation(Online_EWC)algorithm.Experimental results show that,compared with unconstrained incremental learning,incremental learning using EWC and Online_EWC methods can significantly alleviate the problem of catastrophic forgetting and reduce the prediction mean absolute error(MAE)and root mean square error(RMSE),up to 21.7%and 18.3%,respectively.At the same time,the time cost of incremental learning is significantly lower than that of traditional batch learning.关键词
光伏功率预测/长短期记忆(LSTM)网络/增量学习/弹性权重整合(EWC)算法Key words
photovoltaic power prediction/long short-term memory(LSTM)networks/incremental learning/elastic weight consolidation(EWC)algorithm引用本文复制引用
严璐晗,林培杰,程树英,陈志聪,卢箫扬..基于增量学习的CNN-LSTM光伏功率预测[J].电气技术,2024,25(5):31-40,10.基金项目
福建省科技厅引导性基金资助项目(2022H0008)福建省级科技创新重点资助项目(2022G02011) （2022H0008）
