Hybrid Network Model Based on Data Enhancement for Short-term Power Prediction of New PV Plants

作     者：Shangpeng Zhong Xiaoming Wang Bin Xu Hongbin Wu Ming Ding Shangpeng Zhong;Xiaoming Wang;Bin Xu;Hongbin Wu;Ming Ding

作者机构：the Anhui Province Key Laboratory of Renewable Energy Utilization and Energy SavingHefei University of TechnologyHefei 230009China the State Grid Anhui Electric Power Research InstituteHefei 230061China 

出 版 物：《Journal of Modern Power Systems and Clean Energy》 (现代电力系统与清洁能源学报（英文）)

年 卷 期：2024年第12卷第1期

页      面：77-88页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 0807[工学-动力工程及工程热物理] 

基　　金：supported by the Regional Innovation and Development Joint Fund of National Natural Science Foundation of China(No.U19A20106) the Science and Technology Major Projects of Anhui Province(No.202203f07020003) the Science and Technology Project of State Grid Corporation of China(No.52120522000F) 

主　　题：New photovoltaic(PV)plant short-term predic tion time-series generative adversarial network(TimeGAN) hy brid network hyperparameter 

摘      要：This study proposes a hybrid network model based on data enhancement to address the problem of low accuracy in photovoltaic(PV)power prediction that arises due to insuffi cient data samples for new PV ***,a time-series gener ative adversarial network(TimeGAN)is used to learn the distri bution law of the original PV data samples and the temporal correlations between their features,and these are then used to generate new samples to enhance the training ***,a hybrid network model that fuses bi-directional long-short term memory(BiLSTM)network with attention mechanism(AM)in the framework of deep&cross network(DCN)is con structed to effectively extract deep information from the origi nal features while enhancing the impact of important informa tion on the prediction ***,the hyperparameters in the hybrid network model are optimized using the whale optimi zation algorithm(WOA),which prevents the network model from falling into a local optimum and gives the best prediction *** simulation results show that after data enhance ment by TimeGAN,the hybrid prediction model proposed in this paper can effectively improve the accuracy of short-term PV power prediction and has wide applicability.