A scenario-based predictive control approach to building HVAC management systems

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 amb...

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Bibliographic Details
Published in2013 IEEE International Conference on Automation Science and Engineering (CASE 2013) pp. 428 - 435
Main Authors Parisio, Alessandra, Molinari, Marco, Varagnolo, Damiano, Johansson, Karl Henrik
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.08.2013
Subjects
building modeling
building occupancy
Buildings
Computational modeling
Copula
Heating
Mathematical model
Model predictive control
thermal control
Vectors
Weather forecasting
weather forecasts
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Summary: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.
ISBN:9781479915156
1479915157
ISSN:2161-8070
DOI:10.1109/CoASE.2013.6654024