Is learning for the unit commitment problem a low-hanging fruit?

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...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Pineda, S. [verfasserIn] Morales, J.M. [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Unit commitment problem Machine learning Computational burden Power system operations

Übergeordnetes Werk:	Enthalten in: Electric power systems research - Amsterdam [u.a.] : Elsevier Science, 1977, 207
Übergeordnetes Werk:	volume:207

DOI / URN:	10.1016/j.epsr.2022.107851

Katalog-ID:	ELV007607717

Internformat


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520			\|a 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.
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650		4	\|a Computational burden
650		4	\|a Power system operations
700	1		\|a Morales, J.M. \|e verfasserin \|4 aut
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bklnumber	52.52 53.31 53.39
publishDate	2022
allfields	10.1016/j.epsr.2022.107851 doi (DE-627)ELV007607717 (ELSEVIER)S0378-7796(22)00081-5 DE-627 ger DE-627 rda eng 620 DE-600 52.52 bkl 53.31 bkl 53.39 bkl Pineda, S. verfasserin (orcid)0000-0002-1089-0970 aut Is learning for the unit commitment problem a low-hanging fruit? 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. Unit commitment problem Machine learning Computational burden Power system operations Morales, J.M. verfasserin aut Enthalten in Electric power systems research Amsterdam [u.a.] : Elsevier Science, 1977 207 Online-Ressource (DE-627)308447549 (DE-600)1502242-0 (DE-576)259271047 nnns volume:207 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 52.52 Thermische Energieerzeugung Wärmetechnik 53.31 Elektrische Energieübertragung 53.39 Elektrische Energietechnik: Sonstiges AR 207
spelling	10.1016/j.epsr.2022.107851 doi (DE-627)ELV007607717 (ELSEVIER)S0378-7796(22)00081-5 DE-627 ger DE-627 rda eng 620 DE-600 52.52 bkl 53.31 bkl 53.39 bkl Pineda, S. verfasserin (orcid)0000-0002-1089-0970 aut Is learning for the unit commitment problem a low-hanging fruit? 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. Unit commitment problem Machine learning Computational burden Power system operations Morales, J.M. verfasserin aut Enthalten in Electric power systems research Amsterdam [u.a.] : Elsevier Science, 1977 207 Online-Ressource (DE-627)308447549 (DE-600)1502242-0 (DE-576)259271047 nnns volume:207 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 52.52 Thermische Energieerzeugung Wärmetechnik 53.31 Elektrische Energieübertragung 53.39 Elektrische Energietechnik: Sonstiges AR 207
allfields_unstemmed	10.1016/j.epsr.2022.107851 doi (DE-627)ELV007607717 (ELSEVIER)S0378-7796(22)00081-5 DE-627 ger DE-627 rda eng 620 DE-600 52.52 bkl 53.31 bkl 53.39 bkl Pineda, S. verfasserin (orcid)0000-0002-1089-0970 aut Is learning for the unit commitment problem a low-hanging fruit? 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. Unit commitment problem Machine learning Computational burden Power system operations Morales, J.M. verfasserin aut Enthalten in Electric power systems research Amsterdam [u.a.] : Elsevier Science, 1977 207 Online-Ressource (DE-627)308447549 (DE-600)1502242-0 (DE-576)259271047 nnns volume:207 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 52.52 Thermische Energieerzeugung Wärmetechnik 53.31 Elektrische Energieübertragung 53.39 Elektrische Energietechnik: Sonstiges AR 207
allfieldsGer	10.1016/j.epsr.2022.107851 doi (DE-627)ELV007607717 (ELSEVIER)S0378-7796(22)00081-5 DE-627 ger DE-627 rda eng 620 DE-600 52.52 bkl 53.31 bkl 53.39 bkl Pineda, S. verfasserin (orcid)0000-0002-1089-0970 aut Is learning for the unit commitment problem a low-hanging fruit? 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. Unit commitment problem Machine learning Computational burden Power system operations Morales, J.M. verfasserin aut Enthalten in Electric power systems research Amsterdam [u.a.] : Elsevier Science, 1977 207 Online-Ressource (DE-627)308447549 (DE-600)1502242-0 (DE-576)259271047 nnns volume:207 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 52.52 Thermische Energieerzeugung Wärmetechnik 53.31 Elektrische Energieübertragung 53.39 Elektrische Energietechnik: Sonstiges AR 207
allfieldsSound	10.1016/j.epsr.2022.107851 doi (DE-627)ELV007607717 (ELSEVIER)S0378-7796(22)00081-5 DE-627 ger DE-627 rda eng 620 DE-600 52.52 bkl 53.31 bkl 53.39 bkl Pineda, S. verfasserin (orcid)0000-0002-1089-0970 aut Is learning for the unit commitment problem a low-hanging fruit? 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. Unit commitment problem Machine learning Computational burden Power system operations Morales, J.M. verfasserin aut Enthalten in Electric power systems research Amsterdam [u.a.] : Elsevier Science, 1977 207 Online-Ressource (DE-627)308447549 (DE-600)1502242-0 (DE-576)259271047 nnns volume:207 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 52.52 Thermische Energieerzeugung Wärmetechnik 53.31 Elektrische Energieübertragung 53.39 Elektrische Energietechnik: Sonstiges AR 207
language	English
source	Enthalten in Electric power systems research 207 volume:207
sourceStr	Enthalten in Electric power systems research 207 volume:207
format_phy_str_mv	Article
bklname	Thermische Energieerzeugung Wärmetechnik Elektrische Energieübertragung Elektrische Energietechnik: Sonstiges
institution	findex.gbv.de
topic_facet	Unit commitment problem Machine learning Computational burden Power system operations
dewey-raw	620
isfreeaccess_bool	false
container_title	Electric power systems research
authorswithroles_txt_mv	Pineda, S. @@aut@@ Morales, J.M. @@aut@@
publishDateDaySort_date	2022-01-01T00:00:00Z
hierarchy_top_id	308447549
dewey-sort	3620
id	ELV007607717
language_de	englisch
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author	Pineda, S.
spellingShingle	Pineda, S. ddc 620 bkl 52.52 bkl 53.31 bkl 53.39 misc Unit commitment problem misc Machine learning misc Computational burden misc Power system operations Is learning for the unit commitment problem a low-hanging fruit?
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topic_title	620 DE-600 52.52 bkl 53.31 bkl 53.39 bkl Is learning for the unit commitment problem a low-hanging fruit? Unit commitment problem Machine learning Computational burden Power system operations
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title	Is learning for the unit commitment problem a low-hanging fruit?
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title_full	Is learning for the unit commitment problem a low-hanging fruit?
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title_sort	is learning for the unit commitment problem a low-hanging fruit?
title_auth	Is learning for the unit commitment problem a low-hanging fruit?
abstract	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.
abstractGer	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.
abstract_unstemmed	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.
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title_short	Is learning for the unit commitment problem a low-hanging fruit?
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score	7.3998165

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