{"id":"https://openalex.org/W4292829594","doi":"https://doi.org/10.1109/iyce54153.2022.9857544","title":"Investigating the effect of training time for machine learning based photovoltaic power forecasting","display_name":"Investigating the effect of training time for machine learning based photovoltaic power forecasting","publication_year":2022,"publication_date":"2022-07-06","ids":{"openalex":"https://openalex.org/W4292829594","doi":"https://doi.org/10.1109/iyce54153.2022.9857544"},"language":"en","primary_location":{"id":"doi:10.1109/iyce54153.2022.9857544","doi":"https://doi.org/10.1109/iyce54153.2022.9857544","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iyce54153.2022.9857544","pdf_url":null,"source":{"id":"https://openalex.org/S4306424799","display_name":"International Youth Conference on Energy","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 8th International Youth Conference on Energy (IYCE)","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"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"]}],"countries":["HU"],"is_corresponding":false,"raw_author_name":"David Markovics","raw_affiliation_strings":["Budapest University of Technology and Economics,Department of Energy Engineering,Budapest,Hungary","Department of Energy Engineering, Budapest University of Technology and Economics, Budapest, Hungary"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Budapest University of Technology and Economics,Department of Energy Engineering,Budapest,Hungary","institution_ids":["https://openalex.org/I29770179"]},{"raw_affiliation_string":"Department of Energy Engineering, Budapest University of Technology and Economics, 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"]}],"countries":["HU"],"is_corresponding":false,"raw_author_name":"Martin Janos Mayer","raw_affiliation_strings":["Budapest University of Technology and Economics,Department of Energy Engineering,Budapest,Hungary","Department of Energy Engineering, Budapest University of Technology and Economics, Budapest, Hungary"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Budapest University of Technology and Economics,Department of Energy Engineering,Budapest,Hungary","institution_ids":["https://openalex.org/I29770179"]},{"raw_affiliation_string":"Department of Energy Engineering, Budapest University of Technology and Economics, Budapest, Hungary","institution_ids":["https://openalex.org/I29770179"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I29770179"],"apc_list":null,"apc_paid":null,"fwci":1.9666,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.82236935,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":88,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11276","display_name":"Solar Radiation and Photovoltaics","score":0.9994999766349792,"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":0.9994999766349792,"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":9.999999747378752e-05,"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":0.9950000047683716},{"id":"https://openalex.org/keywords/training-duration","display_name":"training duration","score":0.5690000057220459},{"id":"https://openalex.org/keywords/numerical-weather-prediction","display_name":"numerical weather prediction","score":0.953000009059906},{"id":"https://openalex.org/keywords/ensemble-regression","display_name":"ensemble regression","score":0.7770000100135803},{"id":"https://openalex.org/keywords/xgboost","display_name":"XGBoost","score":0.9990000128746033},{"id":"https://openalex.org/keywords/lightgbm","display_name":"LightGBM","score":1.0},{"id":"https://openalex.org/keywords/day-ahead-forecasting","display_name":"day-ahead forecasting","score":0.9390000104904175},{"id":"https://openalex.org/keywords/intraday-forecasting","display_name":"intraday forecasting","score":0.996999979019165},{"id":"https://openalex.org/keywords/seasonal-variation","display_name":"seasonal variation","score":0.6259999871253967},{"id":"https://openalex.org/keywords/forecast-accuracy","display_name":"forecast accuracy","score":0.367000013589859}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.68880295753479},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6047340631484985},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.6046016216278076},{"id":"https://openalex.org/C41291067","wikidata":"https://www.wikidata.org/wiki/Q1897785","display_name":"Photovoltaic system","level":2,"score":0.5990486145019531},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5408317446708679},{"id":"https://openalex.org/C147947694","wikidata":"https://www.wikidata.org/wiki/Q837552","display_name":"Numerical weather prediction","level":2,"score":0.5344528555870056},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.5267502665519714},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.4861709475517273},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.46444395184516907},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.45069557428359985},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.44884192943573},{"id":"https://openalex.org/C2987469573","wikidata":"https://www.wikidata.org/wiki/Q182868","display_name":"Weather prediction","level":2,"score":0.4288899302482605},{"id":"https://openalex.org/C21001229","wikidata":"https://www.wikidata.org/wiki/Q182868","display_name":"Weather forecasting","level":2,"score":0.41822347044944763},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.23380029201507568},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2232367992401123},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.13238075375556946},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.13089513778686523},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iyce54153.2022.9857544","doi":"https://doi.org/10.1109/iyce54153.2022.9857544","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iyce54153.2022.9857544","pdf_url":null,"source":{"id":"https://openalex.org/S4306424799","display_name":"International Youth Conference on Energy","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 8th International Youth Conference on Energy (IYCE)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/7","score":0.571399986743927,"display_name":"Affordable and clean energy"},{"id":"https://metadata.un.org/sdg/13","score":0.5351999998092651,"display_name":"Climate action"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":9,"referenced_works":["https://openalex.org/W2056132907","https://openalex.org/W2095649738","https://openalex.org/W2469734051","https://openalex.org/W2569349941","https://openalex.org/W2792921542","https://openalex.org/W3024158773","https://openalex.org/W3108854659","https://openalex.org/W4220726206","https://openalex.org/W4297957988"],"related_works":["https://openalex.org/W4220726206","https://openalex.org/W4375863191","https://openalex.org/W1918247028","https://openalex.org/W7166808708","https://openalex.org/W4388720134","https://openalex.org/W2787944342","https://openalex.org/W2555637955","https://openalex.org/W4381744701","https://openalex.org/W3021683184","https://openalex.org/W7212175536"],"abstract_inverted_index":{"The":[0,210],"built-in":[1],"capacity":[2],"and":[3,24,108,132,148,165,204,217],"therefore":[4,96],"the":[5,16,33,60,63,68,78,93,97,103,109,113,129,133,136,149,176,184,195,201,207,232,238],"importance":[6],"of":[7,62,67,86,105,112,135,152,178,224,231],"photovoltaic":[8],"(PV)":[9],"power":[10,150,229],"generation":[11],"is":[12,22,59,116,122],"inevitably":[13],"increasing":[14],"in":[15,175,214,222],"past":[17],"years":[18],"resulting":[19],"that":[20,36,138],"it":[21],"less":[23,25],"possible":[26],"to":[27,32,101,124,191,237],"turn":[28],"a":[29,44,81,153],"blind":[30],"eye":[31],"challenging":[34],"problems":[35],"come":[37],"with":[38,55,80,89],"it.":[39],"This":[40,120,188],"article":[41],"focuses":[42],"on":[43,141],"practical":[45,90],"issue":[46],"regarding":[47,92],"numerical":[48],"weather":[49,146],"prediction":[50],"(NWP)":[51],"based":[52],"PV":[53,155],"forecasting":[54,208],"machine":[56],"learning,":[57],"which":[58,75],"effect":[61],"training":[64,94,114],"time.":[65],"Most":[66],"studies":[69],"propose":[70],"new":[71],"high-level":[72],"sophisticated":[73],"algorithms":[74],"can":[76],"reduce":[77],"errors":[79],"given":[82],"percentage,":[83],"however":[84],"few":[85],"them":[87],"deals":[88],"questions":[91],"time":[95,99,219],"operating":[98],"needed":[100],"reach":[102],"accuracy":[104],"physical":[106],"models,":[107],"optimal":[110],"length":[111,173],"dataset":[115],"still":[117],"generally":[118],"unclear.":[119],"study":[121],"supposed":[123],"provide":[125],"some":[126,227],"suggestions":[127],"introducing":[128],"test":[130],"method":[131],"results":[134,211],"simulations":[137],"were":[139,168],"done":[140],"2-years":[142],"data":[143],"including":[144],"spatial":[145],"forecast":[147],"production":[151],"Hungarian":[154],"plant.":[156],"Three":[157],"fast":[158],"but":[159,199],"powerful":[160],"ensemble":[161],"methods,":[162],"XGBoost,":[163],"LightGBM":[164],"ExtraTrees":[166],"regression":[167],"applied":[169],"using":[170],"different":[171],"train":[172],"cases":[174],"range":[177],"2,4,6\u2026":[179],"102":[180],"weeks,":[181],"for":[182,206],"all":[183],"52":[185],"two-week":[186],"intervals.":[187],"simulation":[189],"allows":[190],"investigate":[192],"not":[193],"just":[194],"previously":[196],"mentioned":[197],"aspects,":[198],"also":[200,226],"seasonal":[202],"difference":[203],"sensitivity":[205],"accuracy.":[209],"are":[212],"shown":[213],"both":[215],"day-ahead":[216],"intraday":[218],"horizons,":[220],"where":[221],"case":[223],"latter,":[225],"measured":[228],"values":[230],"close-past":[233],"has":[234],"been":[235],"added":[236],"input":[239],"variables.":[240]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2}],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2022-08-24T00:00:00"}
