Building Energy Efficiency through Advanced Regression Models and Metaheuristic Techniques for Sustainable Management
This study addresses energy consumption and cost reduction in buildings for policymakers and industry stakeholders, but it is incremental as it builds on existing methods.
This research tackled building energy efficiency by using Lasso Regression, Decision Tree, and Random Forest models for energy use forecasting, and applied metaheuristic techniques to enhance Decision Tree, resulting in improved predictive precision.
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.