Occupancy-Driven Energy Optimization: Thermal Modeling and Predictive Control for Office Spaces

This thesis aims to analyze energy savings in environments with unpredictable occupancy rates, presenting a comprehensive analysis of thermal modeling for an office space. This goal is pursued through the use of people-counting sensors, integrated with a control system designed to manage the HVAC terminals. Initially, the current context is analyzed, providing an overview of the main occupancy-based control systems and detection technologies, including commonly used people-counting sensors. Following that, the startup Dropper is introduced along with its innovative solutions for people counting, which play a crucial role in data collection for the developed model. The next phase focuses on the thermal modeling of the building. Using advanced software like EnergyPlus and DesignBuilder, a comprehensive model of the I3P building, the incubator at Politecnico di Torino, was created to get a comprehensive overview of energy consumption. However, to achieve more detailed results, the focus was shifted to a single office. The simulation of this environment yielded data on daily and annual energy consumption. Subsequently, realtime data were gathered inside the office using ENS160 and AHT21 sensors to further refine the digital model. By integrating simulations with actual measurements, a transfer function was developed to create an accurate mathematical model of the office. This model was then employed to implement predictive control (MPC), aiming to minimize deviations from the predefined setpoint temperature. Lastly, the results obtained through MPC were compared with theoretical data from the EnergyPlus simulations, demonstrating how integrating realworld data with simulations can significantly optimize both energy efficiency and indoor comfort.