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. •Categorized the application areas of forecasting models in smart grids.•Summarized presentation of forecasting models for each application area.•Organized discussion on usages and robustness of forecasting models during cyber-attacks.•Presented future directions to develop attack-resilient forecasting models.