LAPSE:2018.0532
Published Article
LAPSE:2018.0532
Reinforcement Learning Based Energy Management Algorithm for Smart Energy Buildings
Sunyong Kim, Hyuk Lim
September 21, 2018
A smart grid facilitates more effective energy management of an electrical grid system. Because both energy consumption and associated building operation costs are increasing rapidly around the world, the need for flexible and cost-effective management of the energy used by buildings in a smart grid environment is increasing. In this paper, we consider an energy management system for a smart energy building connected to an external grid (utility) as well as distributed energy resources including a renewable energy source, energy storage system, and vehicle-to-grid station. First, the energy management system is modeled using a Markov decision process that completely describes the state, action, transition probability, and rewards of the system. Subsequently, a reinforcement-learning-based energy management algorithm is proposed to reduce the operation energy costs of the target smart energy building under unknown future information. The results of numerical simulation based on the data measured in real environments show that the proposed energy management algorithm gradually reduces energy costs via learning processes compared to other random and non-learning-based algorithms.
Keywords
distributed energy resource, Markov decision process, Q-learning, reinforcement learning, renewable energy sources, smart energy building, smart grid
Suggested Citation
Kim S, Lim H. Reinforcement Learning Based Energy Management Algorithm for Smart Energy Buildings. (2018). LAPSE:2018.0532
Author Affiliations
Kim S: School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), 123 Cheomdangwagi-ro, Buk-gu, Gwangju 61005, Korea
Lim H: School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), 123 Cheomdangwagi-ro, Buk-gu, Gwangju 61005, Korea [ORCID]
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Journal Name
Energies
Volume
11
Issue
8
Article Number
E2010
Year
2018
Publication Date
2018-08-02
Published Version
ISSN
1996-1073
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PII: en11082010, Publication Type: Journal Article
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LAPSE:2018.0532
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doi:10.3390/en11082010
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Sep 21, 2018
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CC BY 4.0
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Sep 21, 2018
 
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Calvin Tsay
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