Research on load prediction of back propagation neural networks based on genetic algorithms

被引：0
作者：
Yan L. [1 ]
Mu G. [1 ]
Wang Q. [1 ]
He Z. [1 ]
Zhu Y. [1 ]
机构：
[1] State Grid Shanxi Electric Power Company of China, Shanxi, Taiyuan
来源：
International Journal of High Performance Systems Architecture | 2023年 / 11卷 / 04期
关键词：
BP neural network; electricity load forecasting; electricity market; genetic algorithm; load prediction; local optimality; overfitting;
D O I：
10.1504/IJHPSA.2023.139895
中图分类号：
学科分类号：
摘要：
To address the problem that back propagation (BP) neural networks are prone to overfitting and falling into local optimality, resulting in low accuracy of electricity load forecasting, this paper proposes a method for electricity load forecasting based on an improved genetic algorithm (GA) and the BP neural network. Through modelling and analysis of load data, better root mean square error (RMSE) and mean absolute percentage error (MAPE) are obtained compared with the traditional BP neural networks, proving the method’s superiority. Copyright © 2023 Inderscience Enterprises Ltd.
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页码：198 / 205
页数：7
相关论文
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