The Hidden-Layers Topology Analysis of Deep Learning Models in Survey for Forecasting and Generation of the Wind Power and Photovoltaic Energy

作     者：Dandan Xu Haijian Shao Xing Deng Xia Wang 

作者机构：School of ComputerJiangsu University of Science and TechnologyZhenjiang212003China School of AutomationKey Laboratory of Measurement and Control for CSEMinistry of EducationSoutheast UniversityNanjing210096China School of Information Science and TechnologyNantong UniversityNantong226019China 

出 版 物：《Computer Modeling in Engineering & Sciences》 (工程与科学中的计算机建模（英文）)

年 卷 期：2022年第131卷第5期

页      面：567-597页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：This project is supported by the National Natural Science Foundation of China(NSFC)(Nos.61806087 61902158) 

主　　题：Deep learning wind power forecasting PV generation and forecasting hidden-layer information analysis topology optimization 

摘      要：As wind and photovoltaic energy become more prevalent,the optimization of power systems is becoming increasingly *** current state of research in renewable generation and power forecasting technology,such as wind and photovoltaic power(PV),is described in this paper,with a focus on the ensemble sequential LSTMs approach with optimized hidden-layers topology for short-term multivariable wind power *** methods for forecasting wind power and PV *** physical model,statistical learningmethod,andmachine learning approaches based on historical data are all evaluated for the forecasting of wind power and PV ***,the experiments demonstrated that cloud map identification has a significant impact on PV *** a focus on the impact of photovoltaic and wind power generation systems on power grid operation and its causes,this paper summarizes the classification of wind power and PV generation systems,as well as the benefits and drawbacks of PV systems and wind power forecasting methods based on various typologies and analysis methods.