2026-05-28T17:49:38Zhttps://riuma.uma.es/rest/oai/requestoai:riuma.uma.es:10630/242482026-02-03T11:29:58Zcom_10630_2254col_10630_37953
RIUMA. Repositorio Institucional de la Universidad de Málaga
author
Pineda-Morente, Salvador
author
Morales-González, Juan Miguel
2022-05-31T08:28:18Z
2022-05-31T08:28:18Z
2022-06
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.
https://hdl.handle.net/10630/24248
10.1016/j.epsr.2022.107851
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 to
perceive 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 guide
any strategy to solve it, in particular those that are based on learning from past UCP instances. To this end, we
apply a naive algorithm to produce candidate solutions to the UCP and show, using a variety of realistically sized
power systems, that we are able to find optimal or quasi-optimal solutions with remarkable speedups. To the best
of our knowledge, this is the first work in the technical literature that quantifies how challenging learning the
solution of the UCP actually is for real-size power systems. Our claim is thus that any sophistication of the
learning method must be backed up with a statistically significant improvement of the results in this letter
eng
Atribución 4.0 Internacional
Aprendizaje automático (Inteligencia artificial)
Is learning for the unit commitment problem a low-hanging fruit?
journal article
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