Hybrid Renewable Energy Management Model for Sustainable Building Performance in Urban Environments

This paper presents a hybrid renewable energy management model (HREMM) designed to enhance the performance and sustainability of urban buildings through the intelligent integration of solar, wind, and battery storage systems. Addressing key challenges such as renewable variability, integration costs, and real-time adaptability, the model combines a differential equation-based energy flow framework with long short-term memory (LSTM) networks for predictive load balancing. The system is evaluated using real-time data from Karachi, Munich, and Phoenix over 12 months. Results demonstrate a 93.4 % reliability rate in meeting energy demand, a 15.2 % reduction in grid dependency, and a 10.8 % gain in energy efficiency. Additionally, the system converged to stable operational behaviour within 960 hours, and battery storage maintained an average charge of over 200 kWh without critical faults. These outcomes confirm the model's robustness, adaptability, and potential for deployment in dense urban environments.