Short-term power load forecasting based on an improved multi-verse optimizer algorithm optimized extreme learning machine

被引：0
|
作者：
Long G. [1 ]
Huang M. [1 ]
Fang L. [1 ]
Zheng L. [1 ]
Jiang C. [2 ]
Zhang Y. [2 ]
机构：
[1] Shenzhen Power Supply Bureau Co., Ltd., Shenzhen
[2] School of Electric Power, South China University of Technology, Guangzhou
来源：
Dianli Xitong Baohu yu Kongzhi/Power System Protection and Control | 2022年 / 50卷 / 19期
基金：
中国国家自然科学基金;
关键词：
elite opposition-based learning; extreme learning machine; improved Tent chaotic mapping; multivariate universe optimizer; short-term load forecasting;
D O I：
10.19783/j.cnki.pspc.211708
中图分类号：
学科分类号：
摘要：
To help overcome the shortcomings of short-term power load forecasting caused by random initialization of artificial neural network parameters, a forecasting method based on an improved multivariate universe optimizer (IMVO) algorithm and an extreme learning machine (ELM) is proposed. The improvement of the algorithm includes the following three aspects. First, the improved Tent chaotic mapping method is obtained by adding the random number of beta distribution, and the improved Tent chaotic mapping method with better ergodic uniformity is used to make the MVO algorithm obtain a good initial solution. Second, the travel distance rate of the traditional MVO algorithm is improved using the exponential form, and the improved algorithm can maintain a high global development level in the whole optimization iteration before and during the middle period. Then, the elite reverse learning method is used to improve the universe group. The performance of the algorithm before and after improvement is tested by the benchmark function, indicating that the IMVO algorithm has better stability and robustness. Finally, the IMVO algorithm is used to optimize the weights and thresholds of an ELM, and the IMVO-ELM short-term power load forecasting model is established. Case analysis and comparative experiments show that the stability, prediction accuracy and generalization ability of IMVO-ELM model are better than those of other models. © 2022 Power System Protection and Control Press. All rights reserved.
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页码：99 / 106
页数：7
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