  In the context of global sustainability, buildings are significant consumers
of energy, emphasizing the necessity for innovative strategies to enhance
efficiency and reduce environmental impact. This research leverages extensive
raw data from building infrastructures to uncover energy consumption patterns
and devise strategies for optimizing resource use. We investigate the factors
influencing energy efficiency and cost reduction in buildings, utilizing Lasso
Regression, Decision Tree, and Random Forest models for accurate energy use
forecasting. Our study delves into the factors affecting energy utilization,
focusing on primary fuel and electrical energy, and discusses the potential for
substantial cost savings and environmental benefits. Significantly, we apply
metaheuristic techniques to enhance the Decision Tree algorithm, resulting in
improved predictive precision. This enables a more nuanced understanding of the
characteristics of buildings with high and low energy efficiency potential. Our
findings offer practical insights for reducing energy consumption and
operational costs, contributing to the broader goals of sustainable development
and cleaner production. By identifying key drivers of energy use in buildings,
this study provides a valuable framework for policymakers and industry
stakeholders to implement cleaner and more sustainable energy practices.
