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Energies 2018, 11(8), 2010; https://doi.org/10.3390/en11082010

Reinforcement Learning Based Energy Management Algorithm for Smart Energy Buildings

School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), 123 Cheomdangwagi-ro, Buk-gu, Gwangju 61005, Korea
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Received: 16 July 2018 / Revised: 29 July 2018 / Accepted: 31 July 2018 / Published: 2 August 2018
(This article belongs to the Special Issue Energy Efficiency in Plants and Buildings)
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Abstract

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. View Full-Text
Keywords: smart grid; smart energy building; distributed energy resource; renewable energy sources; Markov decision process; reinforcement learning; Q-learning smart grid; smart energy building; distributed energy resource; renewable energy sources; Markov decision process; reinforcement learning; Q-learning
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Kim, S.; Lim, H. Reinforcement Learning Based Energy Management Algorithm for Smart Energy Buildings. Energies 2018, 11, 2010.

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