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LIAO, Huilian, MICHALENKO, Elizabeth and VEGUNTA, Sarat Chandra (2023). Review of big data analytics for smart electrical energy systems. Energies, 16 (8): 3581.
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Abstract
Energy systems around the world are going through tremendous transformations, mainly driven by carbon footprint reductions and related policy imperatives and low-carbon technological development. These transformations pose unprecedented technical challenges to the energy sector, but they also bring opportunities for energy systems to develop, adapt, and evolve. With rising complexity and increased digitalization, there has been significant growth in the amount of data in the power/energy sector (data ranging from power grid to household levels). Utilization of this large data (or “big data”), along with the use of proper data analytics, will allow for useful insights to be drawn that will help energy systems to deliver an increased amount of technical, operational, economic, and environmental benefits. This paper reviews various categories of data available in the current and future energy systems and the potential benefits of utilizing those data categories in energy system planning and operation. This paper also discusses the Big Data Analytics (BDA) that can be used to process/analyze the data and extract useful information that can be integrated and used in energy systems. More specifically, this paper discusses typical applications of BDA in energy systems, including how BDA can be used to resolve the critical issues faced by the current and future energy network operations and how BDA contributes to the development of smarter and more flexible energy systems. Combining data characterization and analysis methods, this review paper presents BDA as a powerful tool for making electrical energy systems smarter, more responsive, and more resilient to changes in operations.
| Item Type: | Article |
|---|---|
| Contributors: | Editor - Simani, Silvio |
| Additional Information: | ** Article version: VoR ** From MDPI via Jisc Publications Router ** Licence for VoR version of this article: https://creativecommons.org/licenses/by/4.0/ ** Peer reviewed: TRUE **Journal IDs: eissn 1996-1073 **Article IDs: publisher-id: energies-16-03581 **History: published_online 20-04-2023; accepted 19-04-2023; rev-recd 17-04-2023; collection 04-2023; submitted 17-03-2023 |
| Uncontrolled Keywords: | Review, big data analytics, network planning and operation, smart electrical energy systems, demand side management, artificial neural networks, low-carbon technologies |
| Identification Number: | https://doi.org/10.3390/en16083581 |
| SWORD Depositor: | Colin Knott |
| Depositing User: | Colin Knott |
| Date Deposited: | 05 May 2023 16:25 |
| Last Modified: | 05 May 2023 16:25 |
| URI: | https://shura.shu.ac.uk/id/eprint/31845 |
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