Short-Term Electricity-Load Forecasting by deep learning: A comprehensive survey

Short-Term Electricity-Load Forecasting (STELF) refers to the prediction of the immediate demand (in the next few hours to several days) for the power system. Various external factors, such as weather changes and the emergence of new electricity consumption scenarios, can impact electricity demand, causing load data to fluctuate and become non-linear, which increases the complexity and difficulty of STELF. Over the past decade, deep learning, as a key component of implemented artificial intelligence, has been widely applied to STELF, enabling accurate modeling and prediction of electricity demand. This paper provides a comprehensive survey on deep-learning-based STELF over the past ten years. It examines the entire forecasting process, including data pre-processing, feature extraction, deep-learning modeling and optimization, and results evaluation. This paper also identifies key research challenges and potential directions for further investigation in artificial intelligence applications to STELF. • A review of deep learning in Short-Term Electricity-Load Forecasting (STELF). • An analysis across six databases of STELF publication trends. • A detailed explanation of each key step in the STELF process. • A clear summary of STELF research, highlighting trends and key issues.