{"id":"https://openalex.org/W7166808708","doi":"https://doi.org/10.1016/j.egyai.2026.100833","title":"A unified benchmark of deep learning and classical machine learning models for weather-based solar power forecasting: Balancing complexity and skill","display_name":"A unified benchmark of deep learning and classical machine learning models for weather-based solar power forecasting: Balancing complexity and skill","publication_year":2026,"publication_date":"2026-07-01","ids":{"openalex":"https://openalex.org/W7166808708","doi":"https://doi.org/10.1016/j.egyai.2026.100833"},"language":"en","primary_location":{"id":"doi:10.1016/j.egyai.2026.100833","doi":"https://doi.org/10.1016/j.egyai.2026.100833","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.egyai.2026.100833","pdf_url":"https://www.sciencedirect.com/science/article/pii/S266654682600159X/pdf","source":{"id":"https://openalex.org/S4210241048","display_name":"Energy and AI","issn_l":"2666-5468","issn":["2666-5468"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["cwts-core","doaj","dongbi-c","jufo-1","norway-1","oarl","poland-20"],"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Energy and AI","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.sciencedirect.com/science/article/pii/S266654682600159X/pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5090547435","display_name":"D\u00e1vid Markovics","orcid":"https://orcid.org/0009-0005-2848-2296","observed_orcids":["https://orcid.org/0009-0005-2848-2296"]},"institutions":[{"id":"https://openalex.org/I29770179","display_name":"Budapest University of Technology and Economics","ror":"https://ror.org/02w42ss30","country_code":"HU","type":"education","lineage":["https://openalex.org/I29770179"]},{"id":"https://openalex.org/I7597260","display_name":"Hungarian Academy of Sciences","ror":"https://ror.org/02ks8qq67","country_code":"HU","type":"government","lineage":["https://openalex.org/I7597260"]}],"countries":["HU"],"is_corresponding":false,"raw_author_name":"D\u00e1vid Markovics","raw_affiliation_strings":["Department of Energy Engineering, Faculty of Mechanical Engineering, Budapest University of Technology and Economics, M\u0171egyetem rkp. 3, H-1111, Budapest, Hungary","MTA-BME Lend\u00fc let \u201c Momentum\u201d Renewable Energy Systems Research Group, M\u0171egyetem rkp. 3, H-1111, Budapest, Hungary"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Energy Engineering, Faculty of Mechanical Engineering, Budapest University of Technology and Economics, M\u0171egyetem rkp. 3, H-1111, Budapest, Hungary","institution_ids":["https://openalex.org/I29770179"]},{"raw_affiliation_string":"MTA-BME Lend\u00fc let \u201c Momentum\u201d Renewable Energy Systems Research Group, M\u0171egyetem rkp. 3, H-1111, Budapest, Hungary","institution_ids":["https://openalex.org/I29770179","https://openalex.org/I7597260"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055730712","display_name":"Gerg\u0151 Bendeg\u00faz B\u00e9k\u00e9si","orcid":"https://orcid.org/0000-0003-1605-1278","observed_orcids":["https://orcid.org/0000-0003-1605-1278"]},"institutions":[{"id":"https://openalex.org/I29770179","display_name":"Budapest University of Technology and Economics","ror":"https://ror.org/02w42ss30","country_code":"HU","type":"education","lineage":["https://openalex.org/I29770179"]}],"countries":["HU"],"is_corresponding":false,"raw_author_name":"Gerg\u0151 Bendeg\u00faz B\u00e9k\u00e9si","raw_affiliation_strings":["Department of Automation and Applied Informatics, Faculty of Electrical Engineering and Informatics, Budapest University of Technology and Economics, M\u0171egyetem rkp. 3, H-1111, Budapest, Hungary"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Automation and Applied Informatics, Faculty of Electrical Engineering and Informatics, Budapest University of Technology and Economics, M\u0171egyetem rkp. 3, H-1111, Budapest, Hungary","institution_ids":["https://openalex.org/I29770179"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5055995615","display_name":"Martin J\u00e1nos Mayer","orcid":"https://orcid.org/0000-0001-5631-6117","observed_orcids":["https://orcid.org/0000-0001-5631-6117"]},"institutions":[{"id":"https://openalex.org/I29770179","display_name":"Budapest University of Technology and Economics","ror":"https://ror.org/02w42ss30","country_code":"HU","type":"education","lineage":["https://openalex.org/I29770179"]},{"id":"https://openalex.org/I7597260","display_name":"Hungarian Academy of Sciences","ror":"https://ror.org/02ks8qq67","country_code":"HU","type":"government","lineage":["https://openalex.org/I7597260"]}],"countries":["HU"],"is_corresponding":true,"raw_author_name":"Martin J\u00e1nos Mayer","raw_affiliation_strings":["Department of Energy Engineering, Faculty of Mechanical Engineering, Budapest University of Technology and Economics, M\u0171egyetem rkp. 3, H-1111, Budapest, Hungary","MTA-BME Lend\u00fc let \u201c Momentum\u201d Renewable Energy Systems Research Group, M\u0171egyetem rkp. 3, H-1111, Budapest, Hungary"],"raw_orcid":"https://orcid.org/0000-0001-5631-6117","affiliations":[{"raw_affiliation_string":"Department of Energy Engineering, Faculty of Mechanical Engineering, Budapest University of Technology and Economics, M\u0171egyetem rkp. 3, H-1111, Budapest, Hungary","institution_ids":["https://openalex.org/I29770179"]},{"raw_affiliation_string":"MTA-BME Lend\u00fc let \u201c Momentum\u201d Renewable Energy Systems Research Group, M\u0171egyetem rkp. 3, H-1111, Budapest, Hungary","institution_ids":["https://openalex.org/I29770179","https://openalex.org/I7597260"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5055995615"],"corresponding_institution_ids":["https://openalex.org/I29770179","https://openalex.org/I7597260"],"apc_list":{"value":3630,"currency":"USD","value_usd":3630},"apc_paid":{"value":3630,"currency":"USD","value_usd":3630},"fwci":0.8171,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.64786568,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":"25","issue":null,"first_page":"100833","last_page":"100833"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11276","display_name":"Solar Radiation and Photovoltaics","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2105","display_name":"Renewable Energy, Sustainability and the Environment"},"field":{"id":"https://openalex.org/fields/21","display_name":"Energy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11276","display_name":"Solar Radiation and Photovoltaics","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2105","display_name":"Renewable Energy, Sustainability and the Environment"},"field":{"id":"https://openalex.org/fields/21","display_name":"Energy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11052","display_name":"Energy Load and Power Forecasting","score":9.999999747378752e-05,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10468","display_name":"Photovoltaic System Optimization Techniques","score":0.0,"subfield":{"id":"https://openalex.org/subfields/2105","display_name":"Renewable Energy, Sustainability and the Environment"},"field":{"id":"https://openalex.org/fields/21","display_name":"Energy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/solar-power-forecasting","display_name":"photovoltaic power forecasting","score":0.9390000104904175},{"id":"https://openalex.org/keywords/numerical-weather-prediction","display_name":"numerical weather prediction","score":0.8130000233650208},{"id":"https://openalex.org/keywords/photovoltaic-power-generation","display_name":"photovoltaic power generation","score":0.9200000166893005},{"id":"https://openalex.org/keywords/weather-based-forecasting","display_name":"weather-based forecasting","score":0.625},{"id":"https://openalex.org/keywords/deep-learning","display_name":"deep learning","score":0.39100000262260437},{"id":"https://openalex.org/keywords/classical-machine-learning","display_name":"classical machine learning","score":0.9539999961853027},{"id":"https://openalex.org/keywords/recurrent-neural-networks","display_name":"recurrent neural networks","score":0.8500000238418579},{"id":"https://openalex.org/keywords/transformer","display_name":"transformer models","score":0.6439999938011169},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"ensemble learning","score":0.5519999861717224},{"id":"https://openalex.org/keywords/kernel-ridge-regression","display_name":"kernel ridge regression","score":0.7459999918937683},{"id":"https://openalex.org/keywords/model-complexity","display_name":"model complexity","score":0.3149999976158142},{"id":"https://openalex.org/keywords/forecast-skill","display_name":"forecast skill","score":0.5329999923706055},{"id":"https://openalex.org/keywords/hybrid-physical-data-driven-model","display_name":"hybrid physical-data-driven model","score":0.6140000224113464},{"id":"https://openalex.org/keywords/temporal-feature-engineering","display_name":"temporal feature engineering","score":0.640999972820282},{"id":"https://openalex.org/keywords/ecmwf-forecasts","display_name":"ECMWF forecasts","score":0.7059999704360962}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7336999773979187},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6797000169754028},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6376000046730042},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6003000140190125},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5785999894142151},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.36469998955726624},{"id":"https://openalex.org/C50292564","wikidata":"https://www.wikidata.org/wiki/Q2462783","display_name":"Computational learning theory","level":3,"score":0.3093000054359436},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2888000011444092},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2597000002861023},{"id":"https://openalex.org/C24138899","wikidata":"https://www.wikidata.org/wiki/Q17141258","display_name":"Instance-based learning","level":3,"score":0.2531000077724457}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1016/j.egyai.2026.100833","doi":"https://doi.org/10.1016/j.egyai.2026.100833","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.egyai.2026.100833","pdf_url":"https://www.sciencedirect.com/science/article/pii/S266654682600159X/pdf","source":{"id":"https://openalex.org/S4210241048","display_name":"Energy and AI","issn_l":"2666-5468","issn":["2666-5468"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["cwts-core","doaj","dongbi-c","jufo-1","norway-1","oarl","poland-20"],"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Energy and AI","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1016/j.egyai.2026.100833","doi":"https://doi.org/10.1016/j.egyai.2026.100833","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.egyai.2026.100833","pdf_url":"https://www.sciencedirect.com/science/article/pii/S266654682600159X/pdf","source":{"id":"https://openalex.org/S4210241048","display_name":"Energy and AI","issn_l":"2666-5468","issn":["2666-5468"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["cwts-core","doaj","dongbi-c","jufo-1","norway-1","oarl","poland-20"],"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Energy and AI","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.9786999821662903,"display_name":"Climate action","id":"https://metadata.un.org/sdg/13"},{"score":0.9090999960899353,"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7"}],"study_designs":[],"awards":[{"id":"https://openalex.org/G304784885","display_name":null,"funder_award_id":"OTKA-FK 142702","funder_id":"https://openalex.org/F4320322193","funder_display_name":"Nemzeti Kutat\u00e1si \u00e9s Technol\u00f3giai Hivatal"},{"id":"https://openalex.org/G4595574982","display_name":null,"funder_award_id":"KDP-IKT-2023-900-I1-00000957/0000003","funder_id":"https://openalex.org/F4320322192","funder_display_name":"Magyar Tudom\u00e1nyos Akad\u00e9mia"},{"id":"https://openalex.org/G5348832316","display_name":null,"funder_award_id":"OTKA-FK 142702","funder_id":"https://openalex.org/F4320335908","funder_display_name":"Nemzeti Kutat\u00e1si, Fejleszt\u00e9si \u00e9s Innovaci\u00f3s Alap"},{"id":"https://openalex.org/G870192659","display_name":null,"funder_award_id":"KDP-IKT-2023-900-I1-00000957/0000003","funder_id":"https://openalex.org/F4320335908","funder_display_name":"Nemzeti Kutat\u00e1si, Fejleszt\u00e9si \u00e9s Innovaci\u00f3s Alap"}],"funders":[{"id":"https://openalex.org/F4320321994","display_name":"Hungarian Scientific Research Fund","ror":"https://ror.org/00v349e63"},{"id":"https://openalex.org/F4320322192","display_name":"Magyar Tudom\u00e1nyos Akad\u00e9mia","ror":"https://ror.org/02ks8qq67"},{"id":"https://openalex.org/F4320322193","display_name":"Nemzeti Kutat\u00e1si \u00e9s Technol\u00f3giai Hivatal","ror":"https://ror.org/03g2am276"},{"id":"https://openalex.org/F4320335908","display_name":"Nemzeti Kutat\u00e1si, Fejleszt\u00e9si \u00e9s Innovaci\u00f3s Alap","ror":null}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7166808708.pdf","grobid_xml":"https://content.openalex.org/works/W7166808708.grobid-xml"},"referenced_works_count":69,"referenced_works":["https://openalex.org/W1789155650","https://openalex.org/W1924770834","https://openalex.org/W1968054560","https://openalex.org/W1969911362","https://openalex.org/W1996115698","https://openalex.org/W2031939255","https://openalex.org/W2033406960","https://openalex.org/W2056132907","https://openalex.org/W2056192250","https://openalex.org/W2064675550","https://openalex.org/W2089056028","https://openalex.org/W2098677588","https://openalex.org/W2101234009","https://openalex.org/W2117178635","https://openalex.org/W2295598076","https://openalex.org/W2296521892","https://openalex.org/W2425305442","https://openalex.org/W2469734051","https://openalex.org/W2766736793","https://openalex.org/W2896248501","https://openalex.org/W2911964244","https://openalex.org/W2951718281","https://openalex.org/W2959101608","https://openalex.org/W3015376158","https://openalex.org/W3016208458","https://openalex.org/W3024509612","https://openalex.org/W3108854659","https://openalex.org/W3118310392","https://openalex.org/W3124627370","https://openalex.org/W3127414361","https://openalex.org/W3133901049","https://openalex.org/W3173539742","https://openalex.org/W3177318507","https://openalex.org/W3200008329","https://openalex.org/W3204713906","https://openalex.org/W3210135557","https://openalex.org/W3211382940","https://openalex.org/W3215258715","https://openalex.org/W4220726206","https://openalex.org/W4220864797","https://openalex.org/W4225494949","https://openalex.org/W4282840894","https://openalex.org/W4284972439","https://openalex.org/W4285384244","https://openalex.org/W4291327732","https://openalex.org/W4312527766","https://openalex.org/W4362661776","https://openalex.org/W4377090508","https://openalex.org/W4386881259","https://openalex.org/W4387879092","https://openalex.org/W4388031431","https://openalex.org/W4390690789","https://openalex.org/W4391040852","https://openalex.org/W4391870784","https://openalex.org/W4392157308","https://openalex.org/W4392352804","https://openalex.org/W4392506574","https://openalex.org/W4396213642","https://openalex.org/W4399559342","https://openalex.org/W4399660747","https://openalex.org/W4400482398","https://openalex.org/W4404851540","https://openalex.org/W4405854600","https://openalex.org/W4412188095","https://openalex.org/W4413143962","https://openalex.org/W6889955440","https://openalex.org/W7081912476","https://openalex.org/W7131677077","https://openalex.org/W7154839529"],"related_works":["https://openalex.org/W4220726206","https://openalex.org/W4417235590","https://openalex.org/W4393854769","https://openalex.org/W3210809298","https://openalex.org/W4313681190","https://openalex.org/W6947854576","https://openalex.org/W4280539216","https://openalex.org/W3165142360","https://openalex.org/W3049279374","https://openalex.org/W4411449013"],"abstract_inverted_index":{"Numerical":[0],"weather":[1,94],"prediction":[2],"(NWP)":[3],"is":[4,62,109],"the":[5,38,121,207,214,222],"primary":[6],"input":[7],"for":[8,14,48,82],"forecasting":[9],"photovoltaic":[10],"(PV)":[11],"power":[12,91],"generation":[13],"lead":[15],"times":[16],"ranging":[17],"from":[18,73],"a":[19,113],"few":[20],"hours":[21],"to":[22,64,230],"several":[23],"days.":[24],"Data-driven":[25],"and":[26,57,80,92,102,144,154,163,171,190,221,227,238,247],"hybrid":[27],"irradiance-to-power":[28],"methods":[29],"are":[30,78,133,167,194,240],"considered":[31,106],"state-of-the-art;":[32],"however,":[33],"it":[34],"remains":[35],"unclear":[36],"whether":[37],"latest":[39],"improvements":[40],"in":[41],"machine":[42],"learning":[43,59,175],"(ML)":[44],"hold":[45],"significant":[46,215],"potential":[47],"this":[49],"domain.":[50],"A":[51],"comparison":[52],"of":[53,88,111,116,124,210,218,225,245],"12":[54],"classical":[55,159],"ML":[56,107],"deep":[58],"(DL)":[60],"models":[61,72,77,220,229],"presented":[63],"compare":[65],"sequence-to-sequence":[66],"architectures":[67],"with":[68,187],"feature-engineered":[69],"tabular":[70],"regression":[71,166],"various":[74],"aspects.":[75],"All":[76],"implemented":[79],"optimized":[81],"seven":[83],"locations,":[84],"using":[85],"four":[86],"years":[87],"measured":[89],"PV":[90],"ECMWF":[93],"forecasts.":[95],"Results":[96],"reveal":[97],"that":[98,201],"after":[99],"careful":[100],"implementation":[101],"proper":[103],"optimization,":[104],"each":[105],"model":[108,127,202],"capable":[110],"achieving":[112],"skill":[114,123],"score":[115],"46.7-47.7%":[117],"over":[118],"persistence,":[119],"outperforming":[120],"40.3%":[122],"reference":[125],"physical":[126,188,234],"chains.":[128],"The":[129,196],"most":[130],"accurate":[131],"forecasts":[132],"created":[134],"by":[135,151],"recurrent":[136,146],"neural":[137],"networks":[138],"(RNN),":[139],"including":[140,213],"long":[141],"short-term":[142],"memory":[143],"gated":[145],"unit":[147],"setups,":[148],"closely":[149],"followed":[150],"Pyraformer,":[152],"Informer,":[153],"Autoformer":[155],"transformer":[156,228],"variants.":[157],"However,":[158],"multilayer":[160],"perceptrons":[161],"(MLP)":[162],"kernel":[164],"ridge":[165],"also":[168],"great":[169],"alternatives,":[170],"even":[172],"easy-to-use":[173],"ensemble":[174,226],"algorithms,":[176],"such":[177],"as":[178,242],"ExtraTrees":[179],"(ET),":[180],"can":[181],"catch":[182],"up":[183],"if":[184],"expert":[185],"inputs":[186],"hybridization":[189],"temporal":[191],"feature":[192],"engineering":[193],"provided.":[195],"similar":[197],"forecast":[198],"quality":[199],"suggests":[200],"selection":[203],"should":[204],"rely":[205],"on":[206],"joint":[208],"consideration":[209],"multiple":[211],"factors,":[212],"computational":[216],"complexity":[217],"DL":[219],"large":[223],"sensitivity":[224],"their":[231],"hyperparameters.":[232],"Overall,":[233],"modeling,":[235],"ET,":[236],"MLP,":[237],"RNN":[239],"emerging":[241],"Pareto-optimal":[243],"trade-offs":[244],"accuracy":[246],"runtime.":[248]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-11-10T11:39:43.021295","created_date":"2026-07-02T00:00:00"}