Data-Driven Approaches to Analyze Energy Consumption Using Linear Regression Algorithm

In this analysis, there is a tackling of the challenge in optimizing utility of energy consumption using a linear search algorithm. The problem involves minimizing energy waste while maintaining service efficiency. The ideal solution helps in predictive analytics to anticipate energy demand and adjust supply accordingly. The data comprises historical energy consumption records, weather patterns, and operational parameters. The process involves data preprocessing, feature selection, and applying linear search for initial optimization. The approach enhances accuracy, robustness in energy management and it was evaluated on metrics such as prediction accuracy and scalability. The findings of this work indicate significant potential for reducing energy wastage and improving operational efficiency. This study examines critical factors influencing energy use and explores strategies for optimization, such as upgrading to energyefficient equipment and incorporating renewable energy sources. The research highlights opportunities for cost savings, reduced environmental impact, and decreased reliance on fossil fuels, providing a framework for sustainable and efficient energy management.