An Experiential-Led Predictive Control for Enhancing Occupant Thermal Comfort and Reducing Energy Consumption of Heating Systems

To decarbonize space heating systems, energy efficiency improvement plays the same important role as adopting emerging clean energy technologies. Traditional space heating control systems are often developed under the assumption of uniform temperature distribution within a bounded space without considering large thermal inertias, which leads to much delayed control actions. This paper presents a human experiential-led predictive control strategy to achieve the desired temperature around the occupant position which, hence, enhances occupants' thermal comfort and may reduce energy consumption. The work starts from developing a Multiphysics 3D Computational Fluid Dynamics (CFD) thermal model using a test room parameter. Then a temperature prediction model is derived to estimate the thermal delay time and the relationship between the temperature overshoot and switching time/temperature. With the estimated time delay and overshoot at various heater switching time, the performance of the predictive control system is evaluated through multi-software platform integrated simulations. Compared with those traditional space heating control strategies based on uniform space temperature distribution, the simulation results indicate that the experiential-led predictive control could achieve over 40% energy savings while significantly enhancing occupants' thermal comfort in certain applications.