太阳能Issue(4)：69-75,7.DOI:10.19911/j.1003-0417.tyn20240424.02
基于Inception-CNN-LSTM的光伏发电输出功率预测模型研究
RESEARCH ON PREDICTION MODEL OF PV POWER GENERATION OUTPUT BASED ON INCEPTION-CNN-LSTM
摘要
Abstract
Output power,as an important indicator for the operation and maintenance of PV power generation systems,is an important way to understand the power generation capacity and operating conditions of PV power generation systems.This paper combines the multi-scale feature extraction ability of Inception network,the local feature capture ability of convolutional neural network(CNN),and the time series modeling ability of long short term memory network(LSTM)to propose a PV power generation system output power prediction model based on Inception CNN-LSTM,and compares its prediction accuracy with the other three models.The research results show that the Inception-CNN-LSTM model outperforms traditional LSTM models,CNN-LSTM models,and random forest models in terms of average absolute percentage error,root mean square error coefficient of variation,and model fitting index.This model has broad application prospects in power dispatching,fault diagnosis,and PV module operation and maintenance,and can provide strong support for the efficient operation of PV power generation systems.关键词
光伏发电/输出功率预测/卷积神经网络/长短期记忆网络/神经网络Key words
PV power/output power forecasting/CNN/LSTM/neural network分类
信息技术与安全科学引用本文复制引用
张芸芸,陈家乐,李铮伟..基于Inception-CNN-LSTM的光伏发电输出功率预测模型研究[J].太阳能,2025,(4):69-75,7.基金项目
住房和城乡建设部科技示范项目(S20200064) （S20200064）
