Research on short-term load forecasting of power system based on IWOA-KELM

被引:3
|
作者
Han, Xuesong [1 ]
Shi, Yan [1 ]
Tong, Renjie [2 ]
Wang, Siteng [1 ]
Zhang, Yi [2 ]
机构
[1] State Grid Inner Mongolia East Power Co Ltd, Power Supply Serv Supervis & Support Ctr, Tongliao, Peoples R China
[2] NARI TECH Nanjing Control Syst Co Ltd, Nanjing, Peoples R China
关键词
Power load forecasting; Improved whale algorithm; Extreme learning machine; Prediction accuracy;
D O I
10.1016/j.egyr.2023.05.162
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
A short-term power load forecasting (STPLF) model based on the Improved Whale Optimization Algorithm (IWOA) optimized Kernel Extreme Learning Machine (KELM) is proposed to address the problems of high randomness and low forecasting accuracy of electricity loads. The KELM model is constructed, and the IWOA is used to optimize the core and penalty parameters of the KELM to establish the IWOA-KELM electricity load forecasting model. Combined with the actual data of a certain region, the forecasting analysis results show that the convergence speed and forecasting accuracy of the method are greatly improved compared with IWOA-BP, IWOA-SVM and IWOA-ELM forecasting methods. (c) 2023 The Authors. Published by Elsevier Ltd. This is an open access article under theCCBY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
引用
收藏
页码:238 / 246
页数:9
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