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This study presents a cost-effective intelligent building energy management system that utilizes machine learning and multi-criteria decision-making methodologies for real-time optimization. It incorporates various techniques such as data pre-processing, feature extraction, classification, and secure data handling to enhance energy efficiency while ensuring data privacy and security. The proposed framework aims to minimize greenhouse gas emissions and operational costs, providing actionable insights for sus

Author

dj1255603049

Language

EN