Name
Title | ||
|---|---|---|
A Short-Term and High-Resolution Distribution System Load Forecasting Approach Using Support Vector Regression With Hybrid Parameters Optimization. |
Abstract | ||
|---|---|---|
This paper proposes an approach for distribution system load forecasting, which aims to provide highly accurate short-term load forecasting with high resolution utilizing a support vector regression (SVR) based forecaster and a two-step hybrid parameters optimization method. Specifically, because the load profiles in distribution systems contain abrupt deviations, a data normalization is designed ... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TSG.2016.2628061 | IEEE Transactions on Smart Grid |
Keywords | Field | DocType |
Load forecasting,Load modeling,Forecasting,Optimization,Predictive models,Support vector machines,Training | Particle swarm optimization,Local parameter,Mathematical optimization,Distribution system,Support vector machine,Load forecasting,Engineering,Grid,Database normalization,Traverse | Journal |
Volume | Issue | ISSN |
9 | 4 | 1949-3053 |
Citations | PageRank | References |
7 | 0.53 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Huaiguang Jiang | 1 | 24 | 5.11 |
| Yingchen Zhang | 2 | 97 | 18.22 |
| eduard muljadi | 3 | 14 | 3.85 |
| Jun Jason Zhang | 4 | 122 | 18.78 |
| David Wenzhong Gao | 5 | 75 | 11.70 |