Title | ||
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Deep Reinforcement Learning-Based Demand Response for Smart Facilities Energy Management |
Abstract | ||
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This work proposes a novel deep reinforcement learning (DRL)-based demand response algorithm for smart facilities energy management to minimize electricity costs while maintaining a satisfaction index. Specifically, to accommodate the characteristics of the decision-making problem, long short-term memory (LSTM) units are adopted to extract discriminative features from past electricity price sequen... |
Year | DOI | Venue |
---|---|---|
2022 | 10.1109/TIE.2021.3104596 | IEEE Transactions on Industrial Electronics |
Keywords | DocType | Volume |
Energy management,Load management,Feature extraction,Approximation algorithms,Predictive models,Prediction algorithms,Power system stability | Journal | 69 |
Issue | ISSN | Citations |
8 | 0278-0046 | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
---|---|---|---|
Renzhi Lu | 1 | 0 | 0.34 |
Ruichang Bai | 2 | 0 | 0.34 |
Zhe Luo | 3 | 0 | 0.34 |
Junhui Jiang | 4 | 2 | 1.45 |
Mingyang Sun | 5 | 14 | 8.36 |
Hai-Tao Zhang | 6 | 401 | 37.71 |