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Data mining for energy systems: Review and prospect
Author(s) -
Liu Wenxuan,
Zhao Junhua,
Wang Dianhui
Publication year - 2021
Publication title -
wiley interdisciplinary reviews: data mining and knowledge discovery
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.506
H-Index - 47
eISSN - 1942-4795
pISSN - 1942-4787
DOI - 10.1002/widm.1406
Subject(s) - big data , computer science , cyber physical system , data science , smart grid , electric power system , deep learning , electricity , data modeling , energy (signal processing) , data mining , system integration , artificial intelligence , power (physics) , engineering , statistics , physics , mathematics , quantum mechanics , database , electrical engineering , operating system
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