Energy forecasting is crucial for addressing challenges in data-rich smart grid (SG) systems, encompassing applications such as demand-side management, load shedding, and optimal dispatch. Achieving efficient forecasting with minimal prediction error remains a significant challenge due to the inherent uncertainty in SG data. This paper provides a comprehensive, application-focused review of advanced forecasting methods for SG systems, highlighting recent advancements in probabilistic deep learning (PDL).The review extensively examines traditional point forecasting methods, including statistical, machine learning (ML), and deep learning (DL) techniques, evaluating their suitability for energy forecasting. Additionally, the importance of hybrid approaches and data preprocessing techniques in enhancing forecasting performance is discussed.A comparative case study utilizing the Victorian electricity consumption in Australia and American Electric Power (AEP) datasets is conducted to assess the performance of deterministic and probabilistic forecasting methods. The analysis reveals that DL methods, with appropriate hyper-parameter tuning, exhibit superior efficacy when dealing with larger sample sizes and nonlinear patterns. Moreover, PDL methods demonstrate at least a 60% reduction in prediction errors compared to other benchmark DL methods. However, the increased execution time for PDL methods, due to the large sample space, necessitates a balance between computational performance and forecasting accuracy.
Keywords: Grid, Management, Strategies., advanced forecasting techniques
