热力发电2023，Vol.52Issue(11)：123-131,9.DOI:10.19666/j.rlfd.202301023
基于相似日聚类的超短期光伏功率组合预测模型
Ultrashort term photovoltaic power combinatorial forecasting model based on similar day clustering
摘要
Abstract
Aiming at the problem of low prediction accuracy of single power prediction model due to the impact of photovoltaic power fluctuation,a combined photovoltaic power prediction model based on similar day clustering is proposed.Firstly,k-means clustering is selected to divide the original power data into three similar day sample sets of sunny,rainy and cloudy according to different weather types,and the variational mode decomposition(VMD)is used to decompose the similar day samples;Secondly,the convolution neural network is used to optimize the support vector machine(CNN-SVM)and bidirectional short-term and short-term memory(BiLSTM)neural network,respectively,to predict and superimpose the decomposed power data and combine the prediction results with weights,and the grid search algorithm(GS)is used to find the optimal combination weight to improve the performance of the combination prediction model.Finally,the validity of the PV power prediction model proposed in this paper is verified by the one-year measured data of a photovoltaic power station in Australia.The experimental results show that the model proposed in this paper can predict the photovoltaic power well and has strong adaptability no matter what weather type.关键词
光伏功率预测/卷积神经网络/支持向量机/长短时记忆神经网络/网格搜索算法Key words
PV power prediction/CNN/SVM/LSTM neural network/grid search algorithm引用本文复制引用
常青松,杨昭,杨熠辉,雷阳,何信林..基于相似日聚类的超短期光伏功率组合预测模型[J].热力发电,2023,52(11):123-131,9.基金项目
中国华能集团有限公司总部科技项目(HNKJ22-H36)Science and Technology Project of China(HNKJ22-H36) （HNKJ22-H36）
