Resiliency of forecasting methods in different application areas of smart grids: A review and future prospects

Article

Rahman, M.A., Islam, Md Rashidul, Hossain, Md Alamgir, Rana, M.S., Hossain, Md Jahangir and Gray, Evan MacA.. 2024. "Resiliency of forecasting methods in different application areas of smart grids: A review and future prospects." Engineering Applications of Artificial Intelligence. 135. https://doi.org/10.1016/j.engappai.2024.108785
Article Title

Resiliency of forecasting methods in different application areas of smart grids: A review and future prospects

ERA Journal ID32032
Article CategoryArticle
AuthorsRahman, M.A., Islam, Md Rashidul, Hossain, Md Alamgir, Rana, M.S., Hossain, Md Jahangir and Gray, Evan MacA.
Journal TitleEngineering Applications of Artificial Intelligence
Journal Citation135
Article Number108785
Number of Pages24
Year2024
PublisherElsevier
Place of PublicationUnited Kingdom
ISSN0952-1976
1873-6769
Digital Object Identifier (DOI)https://doi.org/10.1016/j.engappai.2024.108785
Web Address (URL)https://www.sciencedirect.com/science/article/pii/S0952197624009436
Abstract

The cyber–physical infrastructure of a smart grid requires data-dependent artificial intelligence (AI)-based forecasting schemes for predicting different aspects for the short- to long-term, where AI-based schemes include machine learning (ML), deep learning (DL), and hybrid models. These forecasting schemes in different application areas of a smart grid can be vulnerable to cyber-attacks, which is yet to be addressed from a broad perspective. This work reviews the literature addressing the vulnerability of forecasting schemes in smart grids with a categorization of application areas. The existing research works addressing cyber-security or cyber resiliency are reviewed and then presented in an organized manner according to application areas to highlight their advantages and disadvantages. The findings of this review indicate a critical need to develop accurate and robust AI-based forecasting schemes capable of withstanding diverse attack scenarios in each sector, while addressing unsymmetrical attention to different sectors of smart grids. Hence, this review provides a comprehensive overview of the current literature and emphasizes the necessity for the research community to advance toward developing attack-resilient AI-based forecasting schemes designed explicitly for smart grids.

KeywordsCyber-attack; Forecasting; Renewable energies; Smart grid; Artificial intelligence
Contains Sensitive ContentDoes not contain sensitive content
ANZSRC Field of Research 2020400803. Electrical energy generation (incl. renewables, excl. photovoltaics)
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Byline AffiliationsHongik University, Korea
Rajshahi University of Engineering and Technology, Bangladesh
Griffith University
University of Technology Sydney
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