期刊
IEEE ACCESS
卷 7, 期 -, 页码 82194-82205出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2019.2924030
关键词
Artificial intelligence; deep reinforcement learning; demand response (DR); industrial facilities; actor-critic
资金
- National Research Foundation of Korea under the Framework of International Cooperation Program, South Korea - China [NRF-2016K2A9A2A11938310]
- National Research Foundation of Korea [NRF-2018K1A3A1A61026320]
As a major consumer of energy, the industrial sector must assume the responsibility for improving energy efficiency and reducing carbon emissions. However, most existing studies on industrial energy management are suffering from modeling complex industrial processes. To address this issue, a model-free demand response (DR) scheme for industrial facilities was developed. In practical terms, we first formulated the Markov decision process (MDP) for industrial DR, which presents the composition of the state, action, and reward function in detail. Then, we designed an actor-critic-based deep reinforcement learning algorithm to determine the optimal energy management policy, where both the actor (Policy) and the critic (Value function) are implemented by the deep neural network. We then confirmed the validity of our scheme by applying it to a real-world industry. Our algorithm identified an optimal energy consumption schedule, reducing energy costs without compromising production.
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