Smart Energy Optimization using Random Forest for Accurate Forecasting and Sustainable Growth

This Project seeks to transform energy management in commercial structures by employing cutting-edge machine learning algorithms to forecast energy usage and improve sustainability. The system uncovers complex patterns often overlooked by conventional methods through the analysis of past energy consumption data, climate information, and operational timetables. Random Forest proved to be the most precise among the tested models, which included Decision Tree, Random Forest, and Linear Regression. Beyond optimizing energy consumption and decreasing operational expenses, the system also promotes eco-friendly practices. This methodology supersedes inefficient manual procedures and rule-of-thumb approaches, offering a flexible and reliable answer to contemporary commercial energy management challenges.