A predictive system based on machine learning has been developed to enhance building energy efficiency while preserving environmental sustainability

Abstract

Modern society encounters vital energy efficiency and sustainability challenges because rising energy consumption while preserving environmental protection continues to be essential. Modern machine learning algorithms deployed in predictive implementation forecast energy consumption across multiple buildings to enhance sustainable resource management platforms. A predictive model links weather information with building specifications and occupancy profiles and energy consumption histories to build a predictive framework that can scale across multiple facilities. The solution solution depends on machine learning algorithms consisting of Gradient Boosting Random Forest and Deep Neural Networks that track intricate user patterns to produce exact energy consumption predictions using specified parameter formats. Feature engineering and hyperparameter optimization automatically produce a model forecast system to provide better accuracy in varying scenarios yet it maintains reliable performance. The comparison between these systems establishes their operational readiness while demonstrating flexible design features and efficient performance capabilities. With correct data inputs the system lets decision-makers maximize energy efficiency and minimize both operational expenses and environmental challenges. Researchers study peak demand patterns to establish flexible energy-conserving plans and adjustable dynamic protocols for smart building energy management. The usage of machine learning models for energy predictions exceeds statistical methods which produces sustained advancements in sustainable energy implementation. The project showcases artificial intelligence systems resolving energy concerns worldwide while supporting the creation of sustainable buildings.