A Scenario-based Predictive Control Approach to Building HVAC Management Systems
2013 (English)In: IEEE International Conference on Automation Science and Engineering, 2013, -435 p.Conference paper (Refereed)
We present a Stochastic Model Predictive Control (SMPC) algorithm that maintains predefined comfort levels in building Heating, Ventilation and Air Conditioning (HVAC) systems while minimizing the overall energy use. The strategy uses the knowledge of the statistics of the building occupancy and ambient conditions forecasts errors and determines the optimal control inputs by solving a scenario-based stochastic optimization problem. Peculiarities of this strategy are that it does not make assumptions on the distribution of the uncertain variables, and that it allows dynamical learning of these statistics from true data through the use of copulas, i.e., opportune probabilistic description of random vectors. The scheme, investigated on a prototypical student laboratory, shows good performance and computational tractability.
Place, publisher, year, edition, pages
2013. -435 p.
Model predictive control, thermal control, weather forecasts, building modeling, building occupancy, copula
IdentifiersURN: urn:nbn:se:kth:diva-136269DOI: 10.1109/CoASE.2013.6654024ScopusID: 2-s2.0-84891506837ISBN: 9781479915156OAI: oai:DiVA.org:kth-136269DiVA: diva2:675729
2013 IEEE International Conference on Automation Science and Engineering, CASE 2013; Madison, WI; United States; 17 August 2013 through 20 August 2013
FunderSwedish Energy AgencyVinnovaKnut and Alice Wallenberg Foundation
QC 201401312013-12-042013-12-042014-01-31Bibliographically approved