Abstract
Advanced data analytics are undoubtedly needed to enable the envisioned smart grid functionalities. Towards that goal, modern statistical learning tools are developed for day-ahead electricity market inference. Congestion patterns are modeled as rank-one components in the matrix of spatio-temporal prices. The new kernel-based predictor is regularized by the square root of the nuclear norm of the sought matrix. Such a regularizer not only promotes low-rank solutions, but it also facilitates a systematic kernel selection methodology. The non-convex optimization problem involved is efficiently driven to a stationary point following a block successive upper bound minimization approach. Numerical tests on real high-dimensional market data corroborate the interpretative merits and the computational efficiency of the novel method.
Original language | English (US) |
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Title of host publication | 2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 7684-7688 |
Number of pages | 5 |
ISBN (Print) | 9781479928927 |
DOIs | |
State | Published - Jan 1 2014 |
Event | 2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014 - Florence, Italy Duration: May 4 2014 → May 9 2014 |
Other
Other | 2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014 |
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Country/Territory | Italy |
City | Florence |
Period | 5/4/14 → 5/9/14 |
Keywords
- Kernel learning
- block successive upper bound minimization
- multikernel selection
- nuclear norm