In the pursuit of optimizing energy management and addressing the challenges of fluctuating energy demand, this study explores the predictive capabilities of machine learning algorithms in forecasting energy demand. The research utilizes a comprehensive dataset obtained from Kaggle, encompassing various temporal and weather-related patterns. The primary objective is to develop robust models that accurately predict energy consumption, thereby aiding in efficient energy planning and resource allocation.T he results demonstrate the comparative effectiveness of these algorithms in predicting energy demand. The findings highlight the significance of incorporating weather-related variables and temporal patterns to enhance the accuracy of energy demand forecasts. The study underscores the potential of advanced machine learning techniques in contributing to the development of more efficient and sustainable energy management systems.

Keywords: Energy Demand Prediction,  Machine Learning Algorithms,  Temporal Patterns, Weather-Related Patterns,  Energy Management,  Forecasting Energy Consumption,  Kaggle Dataset,  Energy Planning,  Resource Allocation