ORCID Profile
0000-0002-5356-7268
Current Organisations
La Trobe University
,
Northeastern University
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Publisher: Elsevier BV
Date: 02-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Elsevier BV
Date: 2019
Publisher: Springer Science and Business Media LLC
Date: 04-08-2023
Publisher: Elsevier BV
Date: 11-2018
Publisher: Elsevier BV
Date: 02-2020
Publisher: Elsevier BV
Date: 12-2018
Publisher: Springer Science and Business Media LLC
Date: 03-05-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: Elsevier BV
Date: 07-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2022
Publisher: Elsevier BV
Date: 08-2019
Publisher: Elsevier BV
Date: 10-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Springer Science and Business Media LLC
Date: 11-08-2022
Publisher: Elsevier BV
Date: 09-2018
Publisher: Elsevier BV
Date: 11-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2017
Publisher: Elsevier BV
Date: 11-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Springer Science and Business Media LLC
Date: 28-04-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2022
Publisher: Wiley
Date: 24-03-2021
DOI: 10.1002/WIDM.1406
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
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2022
No related grants have been discovered for Dianhui Wang.