摘要
阐述电力负荷预测能提高电力系统的运行效率,在智能电网中,随着大数据的海量应用,深度学习以其优异的学习性能,在电力负荷预测领域被广泛采用。探讨深度学习的算法在短期负荷及中长期负荷预测的应用,基于系统特点的对比分析,提出组合算法预测模型的准确度优于单一算法模型。
This paper describes how power load forecasting can improve the operational efficiency of power systems.In smart grids,with the massive application of big data,deep learning has been widely adopted in the field of power load forecasting due to its excellent learning performance.It explores the application of deep learning algorithms in short-term and medium to long-term load forecasting,and based on comparative analysis of system characteristics,proposes that the accuracy of combined algorithm prediction models is better than that of single algorithm models.
作者
梁倩
李公波
LIANG Qian;LI Gongbo(Department of Mechanical and Electronic Engineering,Linyi Campus,Qingdao University of Technology,Shandong 273400,China;School of Management,Shandong University,Shandong 250100,China;State Grid Pingyi Electric Power Company,Shandong 273300,China)
出处
《电子技术(上海)》
2023年第11期46-49,共4页
Electronic Technology