{"title":"Data mining for energy systems: Review and prospect","authors":"Wenxuan Liu, Junhua Zhao, Dianhui Wang","doi":"10.1002/widm.1406","DOIUrl":null,"url":null,"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.","PeriodicalId":48970,"journal":{"name":"Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery","volume":"1 1","pages":""},"PeriodicalIF":6.4000,"publicationDate":"2021-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1002/widm.1406","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 5
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.
期刊介绍:
The goals of Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery (WIREs DMKD) are multifaceted. Firstly, the journal aims to provide a comprehensive overview of the current state of data mining and knowledge discovery by featuring ongoing reviews authored by leading researchers. Secondly, it seeks to highlight the interdisciplinary nature of the field by presenting articles from diverse perspectives, covering various application areas such as technology, business, healthcare, education, government, society, and culture. Thirdly, WIREs DMKD endeavors to keep pace with the rapid advancements in data mining and knowledge discovery through regular content updates. Lastly, the journal strives to promote active engagement in the field by presenting its accomplishments and challenges in an accessible manner to a broad audience. The content of WIREs DMKD is intended to benefit upper-level undergraduate and postgraduate students, teaching and research professors in academic programs, as well as scientists and research managers in industry.