摘要
机器学习与数据驱动方法用于电力系统预测分析,可借大量数据准确预测与管理系统行为,近年因其处理数据及预测能力备受关注。传统电力系统向智能电网转变,让这些方法重要性凸显,而嵌入高比例可再生能源的智能电网过渡有挑战,因可再生能源发电具间歇性。能源互联网促进了这一过渡,整合多种先进数字技术。综述探讨了这些方法在电力系统应用的前景与挑战,及应用预测分析提升系统效率的方式。
Machine learning and data-driven methods are used in predictive analysis of power systems,which can accurately predict and manage system behavior with a large amount of data,and have attracted much attention in recent years because of their data processing and prediction capabilities.The transition from traditional power systems to smart grids makes these approaches more important,and the transition to smart grids embedded with a high proportion of renewable energy is challenging due to the intermittent nature of renewable energy generation.The Internet of Energy facilitates this transition,integrating multiple advanced digital technologies.In this paper,we review the prospects and challenges of these methods in power systems,and how to improve system efficiency by applying predictive analytics.
作者
何继帅
HE Jishuai(State Grid Shanyin County Power Supply Company,Shanyin 036900,China)
出处
《电工技术》
2025年第S1期452-454,457,共4页
Electric Engineering
