An in-depth study on big data mining is urgently needed for the next-generation energy systems, which are characterized by a deep integration of cyber, physical, and social components. This paper presents an initial discussion on big data mining and its applications in intelligent energy systems. New progress in big data mining, such as deep learning, transfer learning, randomized learning, granular computing, and multisource data fusion, is introduced first. Some applications of data mining in energy systems, such as load forecasting and modeling, integrated power and transportation system, and electricity market forecasting and simulation, are discussed then. Moreover, some research problems in energy system data mining, such as cyber–physical–social system modeling and super-resolution perception for smart meter data, which require further attention in the future, are also discussed.

中文翻译：

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能源系统数据挖掘：回顾与展望

下一代能源系统迫切需要对大数据挖掘进行深入研究，其特点是网络、物理和社会成分的深度融合。本文初步讨论了大数据挖掘及其在智能能源系统中的应用。首先介绍深度学习、迁移学习、随机学习、粒计算、多源数据融合等大数据挖掘的新进展。然后讨论了数据挖掘在能源系统中的一些应用，如负荷预测和建模、综合电力和运输系统以及电力市场预测和模拟。此外，能源系统数据挖掘中的一些研究问题，如智能电表数据的网络-物理-社会系统建模和超分辨率感知，