Name
Abstract | ||
|---|---|---|
Demand response (DR) is one of the most cost-effective solutions for providing flexibility to power systems. The extensive deployment of DR trials and the roll-out of smart meters enable the quantification of consumer responsiveness to price signals via baseline estimation. The traditional deterministic baseline estimation approach can provide only a single value without consideration of uncertain... |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/TSG.2019.2895333 | IEEE Transactions on Smart Grid |
Keywords | Field | DocType |
Estimation,Probabilistic logic,Load forecasting,Uncertainty,Load modeling,Data models,Pricing | Data modeling,Data mining,Demand response,Electric power system,Load profile,Control engineering,Artificial intelligence,Deep learning,Engineering,Smart meter,Probabilistic logic,Cluster analysis | Journal |
Volume | Issue | ISSN |
10 | 6 | 1949-3053 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mingyang Sun | 1 | 14 | 8.36 |
| Yi Wang | 2 | 60 | 10.92 |
| Fei Teng | 3 | 10 | 6.45 |
| Yujian Ye | 4 | 17 | 2.86 |
| Goran Strbac | 5 | 5 | 1.80 |
| Chongqing Kang | 6 | 92 | 19.22 |