摘要
中国市场经济和用电设备的快速增长,对电力负荷预测的准确度提出了更高的要求。研究短期电力负荷预测,促进国家电网的发展至关重要。针对电力预测准确度低的问题,提出了基于灰狼优化算法支持向量机的短期负荷智能预测方法,该方法采用灰狼算法对支持向量机的超参数进行优化,构建了短期电力负荷预测模型。以前54d每小时负荷样本为输入数据,预测第55d 24h的负荷,通过对比分析其他的预测模型,表明了所提模型具有更好的预测精度,能够准确地预测出电力负荷。
Due to the rapid growth of China's market economy and electrical equipment,higher requirements have been placed on the accuracy of power load forecasting.Research on short-term load forecasting is needed to promote the development of the State Grid.In response to the problem of low accuracy in power forecasting,a short-term load intelligent prediction method based on the Grey Wolf optimization algorithm support vector machine is proposed.This method uses the Grey Wolf algorithm to optimize the hyperparameters of the support vector machine and constructs a short-term power load prediction model.The previous 54d hourly load samples were used as input data to predict the load on the 55th and 24th day.By comparing and analyzing other prediction models,it was shown that the proposed model has better prediction accuracy and can accurately predict power loads.
作者
段忠炜
褚明洋
刘芳
张丽彬
周嘉诚
龚润泽
刘杰
DUAN Zhongwei;CHU Mingyang;LIU Fang;ZHANG Libin;ZHOU Jiacheng;GONG Runze;LIU Jie(School of Mechanical Engineering and Automation,Wuhan Textile University,Wuhan Hubei 430073,China;Wuhan Xinzhou District Power Supply Company,State Grid Hubei Electric Power Company,Wuhan Hubei 430077,China)
出处
《武汉纺织大学学报》
2025年第6期59-64,共6页
Journal of Wuhan Textile University
基金
国家自然科学基金青年项目(62204178)
国家自然科学基金面上项目(51775388)
湖北省自然科学基金(2022CFB995)
湖北省高等学校优秀中青年科技创新团队项目(T2022015)。
关键词
短期电力负荷预测
灰狼算法
支持向量机
超参数
short-term power load forecasting
grey wolf algorithm
support vector machine
hyperparameter
