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

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.