Name
Title | ||
|---|---|---|
Deep Reinforcement Learning-Based Demand Response for Smart Facilities Energy Management |
Abstract | ||
|---|---|---|
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 |