Tree-Based Algorithms performance in Predicting Household Energy Consumption

Predicting household energy consumption is becoming increasingly important as we strive to manage energy costs and support environmental sustainability. This study takes a close look at how the random forest machine learning method can be used to forecast household energy usage. We used a detailed dataset from the UCI Machine Learning Repository, covering 47 months of minute by-minute energy consumption data. By comparing Random Forest with other popular machine learning techniques like Gradient Boosting, Regression Tree, Support Vector Machine, and Naïve Bayes, we found that Random Forest stood out for its predictive accuracy, achieving 77.05%. While it does take longer to train, the benefits of accuracy make it a strong candidate for practical energy management solutions. Our findings suggest that Random Forest is particularly well-suited for forecasting household energy needs, providing reliable data that could help optimize energy use and craft effective energy-saving strategies. Looking ahead, future research should aim to improve dataset quality and explore advanced optimization techniques to push prediction accuracy even further.