Towards a Cyber-Physical System for Sustainable and Smart Economic Building: A Use Case for Optimizing Water and Energy Consumption

Optimizing energy and water consumption in smart buildings is a critical challenge for enhancing sustainability and reducing operational costs. This paper presents a Cyber-Physical System (CPS) framework that integrates Deep Reinforcement Learning (DRL) and Genetic Algorithms (GA) for real-time decision-making and resource optimization. The system leverages IoT sensors and actuators to monitor and control building systems such as HVAC, lighting, and water management, continuously adjusting parameters to minimize resource consumption while maximizing efficiency. Key findings from the implementation of the DRL + GA framework include up to 20% reductions in energy and water consumption compared to traditional methods. The proposed approach demonstrates significant cost savings and improved system performance, showcasing its effectiveness in real-time optimization. Additionally, the system adapts dynamically to fluctuating conditions such as weather, occupancy, and energy demand. This work contributes to the development of sustainable building management strategies and lays the foundation for smart city applications. The integration of DRL and GA provides a promising solution for optimizing resource allocation and advancing energy efficiency in urban infrastructures.