Name
Title | ||
|---|---|---|
Demand Response Management for Industrial Facilities: A Deep Reinforcement Learning Approach. |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ACCESS.2019.2924030 | IEEE ACCESS |
Keywords | Field | DocType |
Artificial intelligence,deep reinforcement learning,demand response (DR),industrial facilities,actor-critic | Load management,Energy management,Computer science,Efficient energy use,Operations research,Markov decision process,Demand response,Artificial neural network,Energy consumption,Reinforcement learning,Distributed computing | Journal |
Volume | ISSN | Citations |
7 | 2169-3536 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xuefei Huang | 1 | 1 | 0.68 |
| Seung Ho Hong | 2 | 128 | 30.23 |
| Mengmeng Yu | 3 | 12 | 2.69 |
| Yuemin Ding | 4 | 49 | 10.43 |
| Junhui Jiang | 5 | 2 | 1.45 |