Abstract
The accuracy of power load forecasting is a critical factor in ensuring the stability and operational efficiency of power systems. With the rapid advancement of big data and artificial intelligence technologies, these innovations offer novel methods and efficient tools for power load forecasting. This paper provides a systematic review of the application of big data and artificial intelligence in power load forecasting, thoroughly comparing the advantages and limitations of traditional methods and modern techniques, and analyzing the key technical challenges faced today. It focuses on the roles of data preprocessing, feature engineering, and advanced algorithms in optimizing load forecasting and demonstrates the practical effectiveness of these technologies in smart grids through typical case studies. Finally, the paper explores future development trends, identifies key areas for further research, and proposes strategic recommendations. This paper offers theoretical foundations and practical insights for power system planners, researchers, and policymakers, aiming to advance the development of smart grids and optimize power systems.
