Shallow Learning vs Deep Learning in Smart Grid Applications

This chapter discusses real-world applications of shallow learning (SL) and deep learning (DL) in smart grid (SG) applications from traditional energy systems. Here, SGs that depend on SL with structured data, on the one hand, and DL methods for managing unstructured datasets and complex data representations, on the other hand, are examined by comparing their applications in the literature. In practice, SL and DL applications in key SG domains, such as load forecasting, anomaly detection, and energy consumption monitoring, are examined. This chapter also compares hybrid methods for load forecasting and innovative approaches for environmental monitoring in smart cities based on DL and SL with respect to their application in SGs. In particular, it discusses a performance comparison between SL and DL models considering factors such as data size, complexity, and computational requirements. This chapter also presents application insights for DL and SL applications of SG in the critical energy sector to guide future research and applications.