ENERGY DEMAND PREDICTION USING ADVANCED MACHINE LEARNING TECHNIQUES AND REAL TIME WEATHER FEATURES

Abstract

Energy demand forecasting plays a crucial role in modern power systems due to the increasing complexity of electricity consumption patterns influenced by weather conditions, seasonal variations, and human behavior. Traditional statistical methods often fail to accurately capture
these dynamic and non-linear relationships, leading to inefficiencies in energy generation and distribution. This project presents a machine learning-based approach for accurate electricity demand forecasting by integrating historical consumption data, weather parameters, and temporal features