Digital Twin Technology for Enhanced Smart Building Energy Management

The Digital Twin-based Intelligent Building Energy Efficiency Management System represents a cutting-edge approach to optimizing energy usage and enhancing sustainability in modern buildings. This system utilizes a real-time, virtual replica of a building's energy systems to achieve continuous monitoring and management of energy consumption. Central to this system is a Raspberry Pi, which integrates data from various sensors, including temperature, humidity, occupancy, lighting, and air quality sensors. This data is processed and analyzed using linear regression machine learning algorithms to generate actionable insights and predictions.The Digital Twin mirrors the physical environment, enabling real-time adjustments to lighting, ventilation, and other energy-consuming systems based on predicted occupancy and environmental conditions. Actuators such as relays are employed to execute energy-saving measures, which are controlled through a web-based dashboard accessible to building managers. This interactive interface provides a comprehensive view of energy consumption trends and system efficiency, allowing for informed decision-making.By continuously learning and adapting from sensor data, the system refines its predictions and optimizations, leading to ongoing improvements in energy efficiency. This approach not only reduces operational costs but also supports sustainability goals by minimizing energy waste and enhancing the overall environmental performance of the building. The integration of advanced machine learning techniques with real-time sensor data exemplifies a significant advancement in building energy management, positioning this system as a key component in the future of smart building technology.