How machine learning can revolutionize building comfort: Accessing the impact of occupancy prediction models on HVAC control system

Ventilation control systems globally constitute among the most significant energy demands in the building industry. Optimizing the energy usage of such systems is imperative for constructing sustainable buildings and is essential for achieving environmental sustainability. Occupancy factors of these buildings, particularly Heating, Ventilation and Air Conditioning (HVAC) optimization, are pivotal for energy optimization. In this work, we apply machine learning approaches to improve the efficiency of the HVAC systems of the Engineering Classroom and Research Building (ENCARB) at Prairie View A&M University. We focus on supporting real-time automated HVAC control through the accurate estimation of HVAC temperature based on occupancy patterns. Therefore, we introduce AIRFLO, an AI-powered Robust Framework for Learning to Optimize HVAC energy consumption. Our framework integrates several occupancy factors obtained from the class schedule to estimate ideal HVAC temperatures. Specifically, we incorporate user nonspecific behavior vis-a-vis energy HVAC service period within the ENCARB Building to gather useful matrices as the basis for the model design. To learn the complex patterns in data, we trained our framework using supervised machine learning. Specifically, we initially trained our framework using an ensemble of multilayer neural networks using our training data and observed the estimation performance on an independent validation set.  To learn deeper representations and perform systematic comparative model analysis, we proposed our plan to incorporate both Convolutional Neural Networks (CNNs) and Graph Neural Networks (GNNs). Overall, our proposed approach will offer a comprehensive and scalable framework for optimizing HVAC energy optimization, leading to improved sustainability.