Randomized Model Predictive Control for HVAC Systems

Abstract

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.

Implementation of a Scenario-based MPC for HVAC Systems: an Experimental Case Study

“…This tendency is supported not only by simulations (Treado and Chen, 2013;Wallace et al, 2012;Fadzli Haniff et al, 2013), but also by some experimental results on real buildings Široký et al, 2011;Parisio et al, 2013b). Model Predictive Controls (MPCs) may truly yield better comfort levels and energy use performance than current practices do.…”

“…We further define comfort constraints on the indoor CO 2 concentration as 0 ≤ y CO2 (k) ≤ y max CO2 . Considering that x CO2 = y CO2 , comfort constraints and constraints (1) can be written in a compact form as mixed constraints on the input and on the output, V y y CO2 (k) + V u u CO2 (k) ≤ v. We refer the reader to Parisio et al (2013b) for details on the construction of the constraints matrices.…”

“…We notice that the models accurately capture the dynamics of the systems in consideration, and that they constitute an improvement w.r.t. the models considered in Parisio et al (2013b). …”

Control of HVAC systems via scenario-based explicit MPC

“…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.…”

“…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. …”