新型电力系统负荷预测关键技术及多元场景应用  

Key technologies for load forecasting in new power systems and their applications in diverse scenario

作　　者：张冬冬 李芳凝 刘天皓 ZHANG Dongdong;LI Fangning;LIU Tianhao(a.School of Electrical Engineering,Guangxi University,Nanning 530004,China;State Key Laboratory of Featured Metal Materials and Life-Cycle Safety of Composite Structures,Guangxi University,Nanning 530004,China;School of Renewable Energy,Inner Mongolia University of Technology,Ordos 017010,China;Department of Electrical and Electronic Engineering,the University of Hong Kong,Hong Kong 999077,China)

机构地区：[1]广西大学电气工程学院,南宁530004 [2]广西大学省部共建特色金属材料与组合结构全寿命安全国家重点实验室,南宁530004 [3]内蒙古工业大学新能源学院,内蒙古鄂尔多斯017010 [4]香港大学电气与电子工程系,香港999077

出　　处：《综合智慧能源》2025年第3期47-61,共15页Integrated Intelligent Energy

基　　金：国家自然科学基金项目(52107083);广西科技重大专项(AA22068071)。

摘　　要：为实现“双碳”目标,新型电力系统正向绿色化、智能化和多样化转型。负荷预测对保障新型电力系统安全、经济和可靠运行至关重要。尽管传统数理统计方法在规律性明显的负荷数据预测中表现良好,但在新型系统中,高比例可再生能源和随机性用户负荷使数理统计方法面临挑战。人工智能技术,尤其是机器学习和深度学习,因其在处理复杂数据和提取模式方面的优势,成为研究热点,有效提升了负荷预测的准确性和鲁棒性。在此背景下,回顾了基于数理统计原理的负荷预测方法并讨论了其局限性,总结人工智能技术在负荷预测中的应用进展,分析传统机器学习、深度学习及组合预测模型的应用特点。针对区域系统级负荷预测、高比例可再生能源场景下的净负荷预测、多类异质能源协同互补场景下的综合能源系统负荷预测、建筑负荷预测以及电动汽车负荷预测这5类场景下的负荷预测技术难点和关键技术应用进行归纳和总结,对未来负荷预测技术的发展方向进行了展望。To achieve the goal of"dual carbon",the new power system is transitioning towards greening,intelligence,and diversity.Load forecasting is crucial for ensuring the safe,economic,and reliable operation of the new power system.While traditional statistical methods perform well in forecasting load data with clear patterns,the high proportion of renewable energy and the stochastic user load in new power systems pose significant challenges to these methods.Artificial intelligence technologies,particularly machine learning and deep learning,have become research hotspots due to their advantages in dealing with complex data and extracting patterns,effectively improving the accuracy and robustness of load forecasting.In this context,load forecasting methods based on mathematical and statistical principles are reviewed and their limitations are discussed in this study.The latest advancements in applications of AI techniques in load forecasting are summarized,and the characteristics of traditional machine learning,deep learning,and hybrid forecasting models are analysed.Technical challenges of load forecasting and key applications under these five scenarios are summarized and discussed:regional system-level load forecasting,net load forecasting under high proportion of renewable energy scenarios,integrated energy system load forecasting in multi-type heterogeneous energy complementary scenarios,building load forecasting,and electric vehicle load forecasting.The future directions of load forecasting technologies are forecasted.

关 键 词：负荷预测 人工智能 机器学习 深度学习 新型电力系统 电动汽车负荷 综合能源系统负荷 

分 类 号：TK018[动力工程及工程热物理] TM715[电气工程—电力系统及自动化]