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Title:
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Control of a PCM ventilated facade using reinforcement learning techniques
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Author:
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Gracia Cuesta, Alvaro de; Fernàndez Camon, César; Castell, Albert; Mateu Piñol, Carles; Cabeza, Luisa F.
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Notes:
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Artificial intelligence techniques have been successfully applied to control dynamic systems looking for an optimal control. Among those techniques, reinforcement learning has been shown as particularly effective at reducing the dimensionality of some real problems and solving control problems by learning from experience. The use of thermal energy storage active systems in the building sector is identified as suitable option to reduce their energy demand for heating and cooling. However, these systems might be expensive and require appropriate control strategies in order to improve the performance of the building. In this paper a ventilated facade with PCM is controlled using a reinforcement learning algorithm. The ventilated facade uses mechanical ventilation during nighttime to solidify the PCM and releases this cold stored to the inner environment during the peak demand period. It is crucial to decide correctly the schedule of charge and discharge process of the PCM according to the weather and indoor conditions. An experimentally validated numerical model is used to test the performance of the control algorithm under different weather conditions. Important improvements on the energy savings due to the use of control strategies were found and supported by the data under the different tested climatic conditions.
The work partially funded by the Spanish government (ENE2011-28269-C03-01, ENE2011-22722 and ULLE10-4E-1305). The authors would like to thank the Catalan Government for the quality accreditation
given to their research group (2014-SGR-123). The research leading to these results has received funding from the European Union’s Seventh Framework Programme (FP7/2007-2013) under grant agreement #PIRSES-GA-2013-610692 (INNOSTORAGE). Alvaro de Gracia would like to thank Education Ministry of
Chile for Grant PMI ANT1201. |
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Subject(s):
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-Artificial intelligence
-Control system
-Phase change materials (PCM)
-Thermal energy storage (TES) |
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Rights:
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cc-by-nc-nd, (c) Elsevier B.V., 2015
http://creativecommons.org/licenses/by-nc-nd/4.0/
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Document type:
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article
acceptedVersion |
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Published by:
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Elsevier
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Share:
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