基于深度学习的负荷预测研究综述与展望  

Review and Prospect of Load Forecasting Based on Deep Learning

作　　者：马恒瑞 袁傲添 王波[2] 杨昌华 董旭柱 陈来军[1] MA Hengrui;YUAN Aotian;WANG Bo;YANG Changhua;DONG Xuzhu;CHEN Laijun(School of Energy and Electrical Engineering,Qinghai University,Xining 810000,China;School of Electrical Engineering and Automation,Wuhan University,Wuhan 430072,China;School of Electrical Engineering and New Energy,China Three Gorges University,Yichang 443002,China)

机构地区：[1]青海大学能源与电气工程学院,西宁810000 [2]武汉大学电气与自动化学院,武汉430072 [3]三峡大学电气与新能源学院,宜昌443002

出　　处：《高电压技术》2025年第3期1233-1250,共18页High Voltage Engineering

基　　金：国家自然科学基金联合基金(U22A20224);国家自然科学基金(52077109)。

摘　　要：构建新型电力系统是促进现代电力系统转型和发展、实现双碳目标的重要手段,精确的负荷预测结果对于优化电力供需平衡、提升能源利用效率至关重要,以深度学习为代表的人工智能(artificial intelligence,AI)技术可有效优化电力供需平衡,提升能源利用效率。基于此,该文首先从场景对象、数据类型、评价方式、预测方法等角度对负荷预测研究现状进行了分析,并对现有基于深度学习的电力系统负荷预测方法的发展历程、优缺点等进行了系统化评析与总结。最后针对新型电力系统下负荷预测面临的挑战,分别从模型和场景层面对未来技术进行了研究展望。Constructing a new type of power system is an important means to promote the transformation and develop-ment of modern power systems and achieve the dual-carbon goal.Accurate load forecasting results are crucial for optimizing the balance of power supply and demand and enhancing energy utilization efficiency,and artificial intelligence(AI)technology represented by deep learning can effectively optimize the balance of power supply and demand and en-hance energy utilization efficiency.AI technology represented by deep learning can effectively optimize the balance of power supply and demand and improve energy utilization efficiency.Based on this,the paper firstly analyzes the current status of load forecasting research from the perspectives of scene objects,data types,evaluation methods,forecasting methods,etc.,and systematically evaluates and summarizes the development history,advantages and disadvantages of the existing deep learning-based load forecasting methods for power systems.Finally,in view of the challenges of load fore-casting under the new type of power system,the research outlook of the future technology is made from the model and scenario levels,respectively.

关 键 词：新型电力系统 负荷预测 人工智能技术 双碳 神经网络 深度学习 

分 类 号：TM715[电气工程—电力系统及自动化]