Electricity Load

Electricity load forecasting aims to accurately predict energy demand, crucial for efficient grid management and resource allocation. Current research heavily emphasizes the use of advanced machine learning models, such as artificial neural networks (including LSTM and GRU architectures), and hybrid approaches combining these with statistical methods like regression, to improve forecast accuracy across various timescales (from short-term to day-ahead). These models often incorporate diverse input features, including weather data, calendar effects, and even socioeconomic factors, to capture complex patterns in energy consumption. Improved forecasting accuracy translates to significant cost savings, enhanced grid stability, and more effective integration of renewable energy sources.