Heating, Ventilation and Air Conditioning (HVAC) systems play a fundamental role in maintaining acceptable thermal comfort and air quality levels. Model Predictive Control (MPC) techniques are known to bring significant energy savings potential. Developing effective MPC-based control strategies for HVAC systems is nontrivial since buildings dynamics are nonlinear and influenced by various uncertainties. This complicates the use of MPC techniques in practice. We propose to address this issue by designing a stochastic MPC strategy that dynamically learns the statistics of the building occupancy patterns and weather conditions. The main advantage of this method is the absence of a-priori assumptions on the distributions of the uncertain variables, and that it can be applied to any type of building. We investigate the practical implementation of the proposed MPC controller on a student laboratory, showing its effectiveness and computational tractability.
Abstract-Improving energy efficiency of Heating, Ventilation and Air Conditioning (HVAC) systems is a primary objective for the society. Model Predictive Control (MPC) techniques for HVAC systems have recently received particular attention, since they can naturally account for several factors, such as weather and occupancy forecasts, comfort ranges and actuation constraints. Developing effective MPC based control strategies for HVAC systems is nontrivial, since buildings dynamics are nonlinear and affected by various uncertainties. Further, the complexity of the MPC problem and the burden of on-line computations can lead to difficulties in integrating this scheme into a building management system.We propose to address this computational issue by designing a scenario-based explicit MPC strategy, i.e., a controller that is simultaneously based on explicit representations of the MPC feedback law and accounts for uncertainties in the occupancy patterns and weather conditions by using the scenarios paradigm. The main advantages of this approach are the absence of a-priori assumptions on the distributions of the uncertain variables, the applicability to any type of building, and the limited on-line computational burden, enabling practical implementations on low-cost hardware platforms.We illustrate the practical implementation of the proposed explicit MPC controller on a room of a university building, showing its effectiveness and computational tractability. I. INTRODUCTIONHeating, Ventilation and Air Conditioning (HVAC) systems play a fundamental role in maintaining acceptable indoor comfort levels; reports indicate that HVAC systems in developed countries contribute for approximately one fifth of the total national energy usages [1]. Current practice shows its limits, with potential energy savings achievable by using systematic building management being estimated from 5% to 30% of the total consumptions [2], [3]. An effective controller for HVAC systems should incorporate time-dependent energy costs, bounds on the control actions, comfort requirements, as well as account for system uncertainties, e.g., weather conditions and occupancy. A natural scheme that achieves the systematic integration of all the aforementioned elements is the Model Predictive Control (MPC) [4].Simulations in [5], [6], as well as the experimental results on real buildings reported in [7], [8], [9], show that MPC schemes can yield better comfort levels and energy use performance than current practices.
Heating, Ventilation and Air Conditioning (HVAC) systems play a fundamental role in maintaining acceptable thermal comfort and Indoor Air Quality (IAQ) levels, essentials for occupants well-being. Since performing this task implies high energy requirements, there is a need for improving the energetic efficiency of existing buildings. A possible solution is to develop effective control strategies for HVAC systems, but this is complicated by the inherent uncertainty of the to-be-controlled system. To cope with this problem, we design a stochastic Model Predictive Control (MPC) strategy that dynamically learns the statistics of the building occupancy and weather conditions and uses them to build probabilistic constraints on the indoor temperature and CO 2 concentration levels. More specifically, we propose a randomization technique that finds suboptimal solutions to the generally non-convex stochastic MPC problem. The main advantage of this method is the absence of apriori assumptions on the distributions of the uncertain variables, and that it can be applied to any type of building. We investigate the proposed approach by means of numerical simulations and real * tests on a student laboratory, and show its practical effectiveness and computational tractability.
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