Name
Abstract | ||
|---|---|---|
State-of-the-art statistical learning techniques are adapted in this contribution for real-time wind power forecasting. Spatio-temporal wind power outputs are modeled as a linear combination of “few” atoms in a dictionary. By exploiting geographical information of wind farms, a graph Laplacian-based regularizer encourages positive correlation of wind power levels of adjacent farms. Real-time forecasting is achieved by online nonnegative sparse coding with elastic net regularization. The resultant convex optimization problems are efficiently solved using a block coordinate descent solver. Numerical tests on real data corroborate the merits of the proposed approach, which outperforms competitive alternatives in forecasting accuracy. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/CISS.2015.7086873 | Information Sciences and Systems |
Field | DocType | Citations |
Linear combination,Laplacian matrix,Mathematical optimization,Elastic net regularization,Computer science,Wind power forecasting,Solver,Coordinate descent,Convex optimization,Wind power | Conference | 1 |
PageRank | References | Authors |
0.37 | 5 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yu Zhang | 1 | 60 | 7.04 |
| Seung-Jun Kim | 2 | 1003 | 62.52 |
| Giannakis, Georgios B. | 3 | 44 | 7.48 |