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A Data-Driven Approach Towards the Application of Reinforcement Learning Based HVAC Control

  • Refrigeration applications consume a significant share of total electricity demand, with a high indirect impact on global warming through greenhouse gas emissions. Modern technology can help reduce the high power consumption and optimize the cooling control. This paper presents a case study of machine-learning for controlling a commercial refrigeration system. In particular, an approach to reinforcement learning is implemented, trained and validated utilizing a model of a real chiller plant. The reinforcement-learning controller learns to operate the plant based on its interactions with the modeled environment. The validation demonstrates the functionality of the approach, saving around 7% of the energy demand of the reference control. Limitations of the approach were identified in the discretization of the real environment and further model-based simplifications and should be addressed in future research.

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Metadaten
Author:Constantin FalkORCiD, Tarek El Ghayed, Ron van de SandORCiD, Jörg Reiff-StephanORCiDGND
URN:urn:nbn:de:kobv:526-opus4-17066
DOI (Version of Record):https://doi.org/10.46481/jnsps.2023.1244
ISSN:2714-4704
Parent Title (English):Journal of the Nigerian Society of Physical Sciences
Publisher:Department of Physics, Federal University Lafia Nasarawa State, Nigeria ; Nigerian Society of Physical Sciences
Document Type:Article
Language:English
Year of Publication:2023
Publishing Institution:Technische Hochschule Wildau
Release Date:2023/03/01
Tag:Q-learning; optimized control; refrigeration system; reinforcement learning
Volume:5
Issue:1
Article Number:1244
Source:Falk, C., El Ghayed , T. ., van de Sand, R., & Reiff-Stephan, J. (2023). A Data-Driven Approach Towards the Application of Reinforcement Learning Based HVAC Control. Journal of the Nigerian Society of Physical Sciences, 5(1), 1244. https://doi.org/10.46481/jnsps.2023.1244
Faculties an central facilities:Fachbereich Ingenieur- und Naturwissenschaften
Dewey Decimal Classification:6 Technik, Medizin, angewandte Wissenschaften / 62 Ingenieurwissenschaften / 620 Ingenieurwissenschaften und zugeordnete Tätigkeiten
Licence (German):Creative Commons - CC BY - Namensnennung 4.0 International
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