综合智慧能源2025，Vol.47Issue(3)：47-61,15.DOI:10.3969/j.issn.2097-0706.2025.03.005
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
摘要
Abstract
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Key words
load forecasting/artificial intelligence/machine learning/deep learning/new power systems/electric vehicle load/integrated energy system load分类
能源科技引用本文复制引用
张冬冬,李芳凝,刘天皓..新型电力系统负荷预测关键技术及多元场景应用[J].综合智慧能源,2025,47(3):47-61,15.基金项目
国家自然科学基金项目(52107083) （52107083）
广西科技重大专项(AA22068071)National Nature Science Foundation of China(52107083) （AA22068071）
Guangxi Science and Technology Major Program(AA22068071) （AA22068071）
