A Critical Review on Various Short Term Load Forecasting Methods for Distribution Network Planning and Associated Challenges

This review paper investigates the pivotal role of Short-Term Load Forecasting (STLF) in maintaining the crucial balance between electricity supply and demand, a cornerstone of economic energy dispatching. The emergence of renewable energy sources and data-driven methodologies has increased the reliance on demand-side management and Demand Response (DR) programs, whose success and sustainability are decisively impacted by accurate STLF. By predicting-customer consumption, these forecasts empower distribution companies to formulate effective strategies for energy management, infrastructure planning, and budgeting. This work specifically examines the application of Deep Learning (DL) models to the STLF problem, a popular approach due to its ability to handle data volatility, uncertainty, and deliver-high accuracy. The included studies are systematically classified based on their methodologies, dataset specifications, and contributions to uncertainty-aware modeling, online solutions, and practical DR extensions.