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

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

Published in:
Proceedings of the 2014 IFAC World Congress, 19, 1, 599-605
Presented at:
IFAC World Congress, Cape Town International Convention Centre, Cape Town, South Africa, 2014

 Record created 2016-03-11, last modified 2018-09-13

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