IoT-Driven Indoor Wellness Optimization Using a Multivariate Analysis Algorithm

Wellness and comfort are crucial factors that impact occupant health, productivity, and overall satisfaction, and the advent of the Internet of Things (IoT) has brought about a paradigm shift in building management, revolutionizing the integration of advanced sensors and data analytics. This convergence has enabled the development of intelligent building systems that improve energy efficiency and optimize thermal comfort for occupants. In this paper, we propose a novel framework that uses IoT technologies with multivariate analysis (MVA) for the maximization of indoor wellness using IoT data collected from a real environment and correlating them with satisfaction score, obtained through questionnaires. To demonstrate the robustness of the model, we will proceed in two steps. First, with no action control, we will show how the model could be used to effectively identify/classify the environments according to data gathered from IoT, leveraging the soft independent modeling of class analogies (SIMCA) classification method. Secondly, we will show how the control of the temperature(T) could strongly enhance wellness using the same model to predict the optimal indoor temperature. A partial least squares (PLS) regression model is employed for maximizing occupant thermal comfort while optimizing heating, ventilation, and air conditioning (HVAC) energy efficiency. The results demonstrate that the model effectively generalizes across different environments, achieving a satisfaction success rate of 76.47% and significantly reducing the predicted percentage of dissatisfied (PPD) in accordance with Fanger’s thermal comfort model. These findings underscore the model’s robustness, showing a competitive approach to deep learningbased algorithms.