Name
Abstract | ||
|---|---|---|
Unprecedented high volumes of data are becoming available with the growth of the advanced metering infrastructure. These are expected to benefit planning and operation of the future power systems and to help customers transition from a passive to an active role. In this paper, we explore for the first time in the smart grid context the benefits of using deep reinforcement learning, a hybrid type o... |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/TSG.2018.2834219 | IEEE Transactions on Smart Grid |
Keywords | Field | DocType |
Buildings,Machine learning,Learning (artificial intelligence),Energy consumption,Optimization,Smart grids,Minimization | Industrial engineering,Smart grid,Electric power system,Demand response,Control engineering,Schedule,Artificial intelligence,Deep learning,Engineering,Energy consumption,Management system,Reinforcement learning | Journal |
Volume | Issue | ISSN |
10 | 4 | 1949-3053 |
Citations | PageRank | References |
15 | 0.75 | 11 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Elena Mocanu | 1 | 30 | 5.43 |
| Decebal Constantin Mocanu | 2 | 163 | 19.86 |
| H. Nguyen | 3 | 65 | 12.82 |
| Antonio Liotta | 4 | 837 | 90.10 |
| M. E. Webber | 5 | 16 | 1.11 |
| Madeleine Gibescu | 6 | 30 | 7.86 |
| Johannes G. Slootweg | 7 | 39 | 7.73 |