| Autor: |
Falk, Constantin, El Ghayed, Tarek, de Sand, Ron van, Reiff-Stephan, Jorg |
| Předmět: |
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| Zdroj: |
Journal of Nigerian Society of Physical Sciences; Feb2023, Vol. 5 Issue 1, p1-8, 8p |
| Abstrakt: |
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. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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