Predictive thermostat control for office buildings using hybrid AI optimization to enhance energy efficiency and thermal comfort

Heating, ventilation, and air conditioning (HVAC) systems offer the greatest potential for energy savings in building services. However, conventional thermostat control in offices often fails to balance comfort and efficiency. To address this issue, a model predictive control (MPC) framework is proposed to improve thermal comfort in office buildings through predictive thermostat regulation. An Extreme Learning Machine (ELM)-based predictive model is developed to forecast indoor thermal comfort conditions, which is embedded within a receding horizon optimization structure to enable real-time control decisions. To efficiently solve the underlying optimization problem, the Gray Wolf Optimizer (GWO) algorithm is adopted due to its favorable convergence characteristics. A high-fidelity Energy Plus simulation model is constructed to capture the dynamic behavior of the indoor thermal environment, from which comprehensive datasets are generated for model training and validation. The parameters of ELM model are further refined using GWO to enhance forecasting accuracy. The integrated predictive model and MPC strategy are implemented within a co-simulation environment, enabling bidirectional coupling between the MPC controller and the EnergyPlus thermal model. Furthermore, a pilot field experiment is conducted in a real-world office building to validate the applicability of the system. Simulation and experimental results demonstrate that the proposed approach significantly enhances occupant thermal comfort while maintaining energy efficiency, evidencing the effectiveness of the combined data-driven prediction and bio-inspired optimization strategy. The methodological integration of ELM-based prediction, GWO-driven optimization, and practical field validation represents a novel adaptive control framework for enhancing indoor thermal comfort.