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Low-rank kernel learning for electricity market inference.
| Content Provider | CiteSeerX |
|---|---|
| Author | Kekatos, Vassilis Zhang, Yu Giannakis, Georgios B. |
| Abstract | Abstract—Recognizing the importance of smart grid data analytics, modern statistical learning tools are applied here to wholesale electricity market inference. Market clearing congestion patterns are uniquely modeled as rank-one components in the matrix of spatiotemporally correlated prices. Upon postulating a low-rank matrix factorization, kernels across pricing nodes and hours are systematically selected via a novel methodology. To process the high-dimensional market data involved, a blockcoordinate descent algorithm is developed by generalizing blocksparse vector recovery results to the matrix case. Preliminary numerical tests on real data corroborate the prediction merits of the developed approach. I. |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Electricity Market Inference Low-rank Kernel Learning Modern Statistical Learning Tool Low-rank Matrix Factorization Blocksparse Vector Recovery Result Developed Approach Congestion Pattern High-dimensional Market Data Real Data Rank-one Component Preliminary Numerical Test Smart Grid Data Analytics Wholesale Electricity Market Inference Blockcoordinate Descent Algorithm Matrix Case Novel Methodology Prediction Merit |
| Content Type | Text |
