Experimental tests on the performance of an economic model predictive control system in a lightweight building

European policies are fostering the electrification of energy use, including space heating and cooling systems, in order to decarbonise the building stock. The significant penetration of electrical loads and domestic photovoltaic (PV) plants has therefore become an important topic for researchers and engineers working in the building sector. In this context, this paper presents a recently constructed laboratory for testing efficient management strategies in all-electric houses. The article describes the laboratory and the Model Predictive Control (MPC) strategy developed to minimize economic costs for space heating while ensuring thermal comfort in the indoor environment with a simulated rooftop PV system. The proposed controller leverages prior knowledge about the physical and geometric properties of the building and the optimization problem is formulated using mixed-integer quadratic programming. This article reports the results of calibration and optimization performed in open loop, as well as two closed loop tests where the MPC controls the HVAC system using receding horizon. The predictive controller was able to substantially increase PV self-consumption in both tests compared to a conventional thermostat, thus cutting electricity costs for heat pump by 10–17%. Such improvement was obtained at the price of a higher thermal comfort violations, mainly due to oversimplified HVAC system models. In light of these findings, the article analyses the effect of such simplifications and suggests possible alternative modelling approaches.