TY - GEN
T1 - Kernel selection for power market inference via block successive upper bound minimization
AU - Kekatos, Vassilis
AU - Zhang, Yu
AU - Giannakis, Georgios B
PY - 2014
Y1 - 2014
N2 - 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.
AB - 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.
KW - Kernel learning
KW - block successive upper bound minimization
KW - multikernel selection
KW - nuclear norm
UR - http://www.scopus.com/inward/record.url?scp=84905223330&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84905223330&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2014.6855095
DO - 10.1109/ICASSP.2014.6855095
M3 - Conference contribution
AN - SCOPUS:84905223330
SN - 9781479928927
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 7684
EP - 7688
BT - 2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014
Y2 - 4 May 2014 through 9 May 2014
ER -