Name
Abstract | ||
|---|---|---|
For decades, electricity customers have been treated as mere recipients of electricity in vertically integrated power systems. However, as customers have widely adopted distributed energy resources and other forms of customer participation in active dispatch (such as demand response) have taken shape, the value of mining knowledge from customer behavior patterns and using it for power system opera... |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/MIS.2017.3121551 | IEEE Intelligent Systems |
Keywords | Field | DocType |
Forecasting,Data analysis,Photovoltaic systems,Machine learning algorithms,Load modeling,Energy management,Support vector machines | Electricity market,Stand-alone power system,Energy management,Renewable energy,Computer science,Simulation,Knowledge management,Demand response,Distributed generation,Energy consumption,Electricity retailing,Environmental economics | Journal |
Volume | Issue | ISSN |
32 | 4 | 1541-1672 |
Citations | PageRank | References |
3 | 0.38 | 3 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yingchen Zhang | 1 | 97 | 18.22 |
| Rui Yang | 2 | 75 | 18.56 |
| Kaiqing Zhang | 3 | 48 | 13.02 |
| Huaiguang Jiang | 4 | 24 | 5.11 |
| Jun Jason Zhang | 5 | 122 | 18.78 |