Short-term Photovoltaic Power Forecasting Using SOM-based Regional Modelling Methods

作     者：Jun Li Qibo Liu 

作者机构：School of Automation and Electrical EngineeringLanzhou Jiaotong UniversityLanzhou 730070China 

出 版 物：《Chinese Journal of Electrical Engineering》 (中国电气工程学报（英文）)

年 卷 期：2023年第9卷第1期

页      面：158-176页

核心收录：

学科分类：080802[工学-电力系统及其自动化] 0808[工学-电气工程] 08[工学] 

基　　金：Supported by the National Natural Science Foundation of China(51467008) Gansu Provincial Department of Education Industry Support Program(2021CYZC-32) 

主　　题：Photovoltaic power generation forecasting self-organizing map regional modeling extreme learning machine 

摘      要：The inherent intermittency and uncertainty of photovoltaic(PV)power generation impede the development of grid-connected PV *** forecasting PV output power is an effective way to address this problem.A hybrid forecasting model that combines the clustering of a trained self-organizing map(SOM)network and an optimized kernel extreme learning machine(KELM)method to improve the accuracy of short-term PV power generation forecasting are ***,pure SOM is employed to complete the initial partitions of the training dataset;then the fuzzy c-means(FCM)algorithm is used to cluster the trained SOM network and the Davies-Bouldin index(DBI)is utilized to determine the optimal size of clusters,***,in each data partition,the clusters are combined with the KELM method optimized by differential evolution algorithm to establish a regional KELM model or combined with multiple linear regression(MR)using least squares to complete coefficient evaluation to establish a regional MR *** proposed models are applied to one-hour-ahead PV power forecasting instances in three different solar power plants provided by *** with other single global models,the root mean square errors(RMSEs)of the proposed regional KELM model are reduced by 52.06%in plant 1,54.56%in plant 2,and 51.43%in plant 3 on *** results demonstrate that the forecasting accuracy has been significantly improved using the proposed *** addition,the comparisons between the proposed and existing state-of-the-art forecasting methods presented have demonstrated the superiority of the proposed *** forecasts of different methods in different seasons revealed the strong robustness of the proposed *** four seasons,the MAEs and RMSEs of the proposed SF-KELM are generally the ***,the R2 value exceeds 0.9,which is the closest to 1.