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WIREs Data Mining Knowl Discov
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Data mining for energy systems: Review and prospect

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Abstract 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. This article is categorized under: Application Areas > Business and Industry
Modeling and analysis of cyber–physical–social system (CPSS)
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Big data architecture and open source software
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A typical network structure for echo state network (ESN) with one output. Fixed connections are shown as dashed lines, while trainable connections are shown as solid lines
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A typical network structure for random vector functional link (RVFL) with n inputs
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A typical network structure for deep learning
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Framework of super‐resolution perception (SRP)
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