Optimizing HVAC systems with model predictive control: integrating ontology-based semantic models for energy efficiency and comfort

Building systems are dynamic and non-linear. In HVAC systems, independently controlled modules interact, creating complex interdependencies that challenge optimizing energy savings and thermal comfort. Model predictive control (MPC) has emerged as a promising strategy to address these challenges effectively since its inception. In this study, MPC is applied to optimize indoor performance by integrating the district heating and ventilation systems using an ontology-based semantic model, with the objective of minimizing heating energy consumption while maintaining indoor comfort. A data-driven energy model was developed for a single floor of a hospital building, comprising 12 conditioned zones and incorporating data from 45 measuring devices. Two rooms with differing thermal performance and control strategies were selected for analysis. The results demonstrate that the implementation of the MPC reduces heating energy consumption by 7.3% and 8.5% in the respective rooms while also increasing the indoor thermal comfort time by 3.17% and 86.51%, respectively. Integrating MPC with an ontology-based semantic model creates a robust framework for advanced building energy management. This approach facilitates seamless communication and interoperability among HVAC subsystems, enabling cohesive control within a digital twin platform. The semantic model standardizes and contextualizes diverse data, enhancing the accuracy and responsiveness of the MPC.