Scenario-based MPC for energy-efficient building climate control under weather and occupancy uncertainty

Heating, ventilation and air conditioning (HVAC) systems regulate comfort levels in buildings, but also consume a large amount of energy, which makes them an attractive target for efficiency improvements. In this paper, a novel technique called Randomized Model Predictive Control (RMPC) is investigated to improve the control of existing HVAC systems. RMPC uses weather and occupancy predictions to minimize the building´s energy consumption. It accounts for the prediction uncertainties by basing its control actions on a given number of sampled uncertainty scenarios. The main advantage of RMPC over existing methods is the absence of a probabilistic disturbance model. This makes the handling of uncertainties straightforward, even if they are non-Gaussian or non-additive. Moreover, the method of removing adverse samples after solving the initial control problem (RMPC-SR) can lead to a further improvement in the control performance, up to a saturation limit. Although theoretical bounds for choosing the sample sizes are available, our simulations show that only a fraction of these numbers is required for a good performance of RMPC and RMPC-SR. The performance of RMPC and RMPC-SR is investigated through extensive simulations on different models, based on empirically collected data. The results demonstrate that both techniques are attractive alternatives to other Model Predictive Control methods, because they show a higher energy saving potential, and are computationally tractable.