Evaluating and Comparing AI Models for Hourly Energy Demand Prediction

Abstract

This study investigates the application of various AI models to predict energy demand, comparing the performance of four specific models: Decision Tree, Random Forest, Gradient Boosting, and Linear Regression. The evaluation of these models' prediction performance reveals that ensemble methods like Random Forest and Gradient Boosting exhibit promising generalization capabilities, while the Decision Tree model shows high training accuracy but suffers from overfitting. The discussion underscores the importance of ensemble techniques and feature engineering optimization in mitigating overfitting and enhancing forecast accuracy. Furthermore, the study highlights the potential of AI-driven approaches to promote sustainability and resilience in energy systems, emphasizing the need for further optimization and collaboration among stakeholders to achieve a cleaner, more sustainable energy future.