Hybrid SDS and WPT-IBBO-DNM Based Model for Ultra-short Term Photovoltaic Prediction  

作　　者：Hui Hwang Goh Qinwen Luo Dongdong Zhang Hui Liu Wei Dai Chee Shen Lim Tonni Agustiono Kurniawan Kai Chen Goh 

机构地区：[1]School of Electrical Engineering,Guangxi University,Nanning,Guangxi 530004,China [2]University of Southampton Malaysia,Iskandar Puteri 79200,Malaysia [3]College of the Environment and Ecology,Xiamen University,Fujian 361102,China [4]Department of Technology Management,Faculty of Construction Management and Business,University Tun Hussein Onn Malaysia,86400 Parit Raja,Johor,Malaysia

出　　处：《CSEE Journal of Power and Energy Systems》2023年第1期66-76,共11页中国电机工程学会电力与能源系统学报（英文）

基　　金：This work was supported in part by Guangxi University(No.A3020051008);in part by the National Key Research and Development Program of China(No.2019YFE0118000)。

摘　　要：Accurate photovoltaic(PV)power prediction has been a subject of ongoing study in order to address grid stability concerns caused by PV output unpredictability and intermittency.This paper proposes an ultra-short-term hybrid photovoltaic power forecasting method based on a dendritic neural model(DNM)in this paper.This model is trained using improved biogeography-based optimization(IBBO),a technique that incorporates a domestication operation to increase the performance of classical biogeography-based optimization(BBO).To be more precise,a similar day selection(SDS)technique is presented for selecting the training set,and wavelet packet transform(WPT)is used to divide the input data into many components.IBBO is then used to train DNM weights and thresholds for each component prediction.Finally,each component’s prediction results are stacked and reassembled.The suggested hybrid model is used to forecast PV power under various weather conditions using data from the Desert Knowledge Australia Solar Centre(DKASC)in Alice Springs.Simulation results indicate the proposed hybrid SDS and WPT-IBBO-DNM model has the lowest error of any of the benchmark models and hence has the potential to considerably enhance the accuracy of solar power forecasting(PVPF).

关 键 词：Dendritic neural model improved biogeography-based optimization photovoltaic power forecasting similar day selection wavelet packet transform 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]