RT Journal Article
T1 Is learning for the unit commitment problem a low-hanging fruit?
A1 Pineda-Morente, Salvador
A1 Morales-González, Juan Miguel
K1 Aprendizaje automático (Inteligencia artificial)
AB The blast wave of machine learning and artificial intelligence has also reached the power systems community,and amid the frenzy of methods and black-box tools that have been left in its wake, it is sometimes difficult toperceive a glimmer of Occam’s razor principle. In this letter, we use the unit commitment problem (UCP), an NP-hard mathematical program that is fundamental to power system operations, to show that simplicity must guideany strategy to solve it, in particular those that are based on learning from past UCP instances. To this end, weapply a naive algorithm to produce candidate solutions to the UCP and show, using a variety of realistically sizedpower systems, that we are able to find optimal or quasi-optimal solutions with remarkable speedups. To the bestof our knowledge, this is the first work in the technical literature that quantifies how challenging learning thesolution of the UCP actually is for real-size power systems. Our claim is thus that any sophistication of thelearning method must be backed up with a statistically significant improvement of the results in this letter
PB Elsevier
YR 2022
FD 2022-06
LK https://hdl.handle.net/10630/24248
UL https://hdl.handle.net/10630/24248
LA eng
NO S. Pineda, J.M. Morales, Is learning for the unit commitment problem a low-hanging fruit?, Electric Power Systems Research, Volume 207, 2022, 107851, ISSN 0378-7796, https://doi.org/10.1016/j.epsr.2022.107851.
NO This work was supported in part by the Spanish Ministry of Science and Innovation through project PID2020-115460GB-I00, by the Anda-lusian Regional Government through project P20-00153, and by the Research Program for Young Talented Researchers of the University of Málaga under Project B1-2019-11. This project has also received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 755705). Finally, the authors thankfully acknowledge the computer re- sources, technical expertise, and assistance provided by theSCBI (Supercomputing and Bioinformatics) center of the University of Málaga. Funding for open access charge: Universidad de Málaga /CBUA
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 19 ene 2026