Machine Learning Approaches for Load Forecasting and Time Series Analysis

Energy management plays a pivotal role in today’s world, driving sustainable practices and optimizing resource utilization for a brighter, more efficient future. Despite significant advancements, accurately forecasting residential energy consumption remains a challenge due to the complexity of usage patterns and the limitations of existing models. This study explores the application of advanced machine learning techniques for residential energy consumption forecasting through time series analysis. By applying machine learning models like Vector AutoRegression (VAR), XGBoost, LSTM ,and LightGBM models, the research aims to predict usage patterns with high precision. Our findings highlight LightGBM’s superior performance in capturing complex patterns compared to other models, addressing a significant gap in the current understanding of energy consumption forecasting. Specifically, LightGBM achieved a Root Mean Squared Error (RMSE) of 1.37%. The robustness and accuracy of LightGBM in this context underline its potential as a powerful tool for energy management. This proposed model contributes to the development of a user-centric platform for energy management, emphasizing sustainability and cost reduction. By providing detailed insights into consumption patterns, the system facilitates informed decision-making, promoting efficient resource allocation and enhancing grid stability. The work’s strengths lie in its comprehensive approach and the thorough evaluation of multiple models, ensuring the quality and completeness of the findings. These practical implications benefit both energy providers and consumers by optimizing energy consumption and ensuring grid stability, marking a significant step towards sustainable energy management. By effectively addressing the complexities of energy usage patterns and improving forecast accuracy, this study paves the way for more reliable and efficient energy management solutions, fostering a sustainable future for all stakeholders involved.