{"id":"https://openalex.org/W7129068586","doi":"https://doi.org/10.48550/arxiv.2602.13010","title":"Probabilistic Wind Power Forecasting with Tree-Based Machine Learning and Weather Ensembles","display_name":"Probabilistic Wind Power Forecasting with Tree-Based Machine Learning and Weather Ensembles","publication_year":2026,"publication_date":"2026-02-13","ids":{"openalex":"https://openalex.org/W7129068586","doi":"https://doi.org/10.48550/arxiv.2602.13010"},"language":null,"primary_location":{"id":"pmh:doi:10.48550/arxiv.2602.13010","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"publisher-specific-oa","license_id":"https://openalex.org/licenses/publisher-specific-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":null,"any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5118557655","display_name":"Max Bruninx","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Bruninx, Max","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5123865794","display_name":"Diederik van Binsbergen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"van Binsbergen, Diederik","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028997809","display_name":"Timothy Verstraeten","orcid":"https://orcid.org/0000-0003-3036-617X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Verstraeten, Timothy","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126140849","display_name":"Ann Now\u00e9","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Now\u00e9, Ann","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5053092945","display_name":"Jan Helsen","orcid":"https://orcid.org/0000-0002-6574-7629"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Helsen, Jan","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5118557655"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11052","display_name":"Energy Load and Power Forecasting","score":0.9276999831199646,"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"}},"topics":[{"id":"https://openalex.org/T11052","display_name":"Energy Load and Power Forecasting","score":0.9276999831199646,"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/T10680","display_name":"Wind Energy Research and Development","score":0.03970000147819519,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace 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/T10740","display_name":"Wind Turbine Control Systems","score":0.005799999926239252,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/probabilistic-forecasting","display_name":"Probabilistic forecasting","score":0.7889000177383423},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.7642999887466431},{"id":"https://openalex.org/keywords/gradient-boosting","display_name":"Gradient boosting","score":0.7530999779701233},{"id":"https://openalex.org/keywords/wind-power-forecasting","display_name":"Wind power forecasting","score":0.6193000078201294},{"id":"https://openalex.org/keywords/wind-power","display_name":"Wind power","score":0.4977000057697296},{"id":"https://openalex.org/keywords/numerical-weather-prediction","display_name":"Numerical weather prediction","score":0.4837000072002411},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.4729999899864197},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.3905999958515167},{"id":"https://openalex.org/keywords/wind-speed","display_name":"Wind speed","score":0.38989999890327454}],"concepts":[{"id":"https://openalex.org/C122282355","wikidata":"https://www.wikidata.org/wiki/Q7246855","display_name":"Probabilistic forecasting","level":3,"score":0.7889000177383423},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.7642999887466431},{"id":"https://openalex.org/C70153297","wikidata":"https://www.wikidata.org/wiki/Q5591907","display_name":"Gradient boosting","level":3,"score":0.7530999779701233},{"id":"https://openalex.org/C2781084341","wikidata":"https://www.wikidata.org/wiki/Q2583670","display_name":"Wind power forecasting","level":4,"score":0.6193000078201294},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5595999956130981},{"id":"https://openalex.org/C78600449","wikidata":"https://www.wikidata.org/wiki/Q43302","display_name":"Wind power","level":2,"score":0.4977000057697296},{"id":"https://openalex.org/C147947694","wikidata":"https://www.wikidata.org/wiki/Q837552","display_name":"Numerical weather prediction","level":2,"score":0.4837000072002411},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.48330000042915344},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.4729999899864197},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.44269999861717224},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.3905999958515167},{"id":"https://openalex.org/C161067210","wikidata":"https://www.wikidata.org/wiki/Q1464943","display_name":"Wind speed","level":2,"score":0.38989999890327454},{"id":"https://openalex.org/C2781468064","wikidata":"https://www.wikidata.org/wiki/Q1267117","display_name":"Lead time","level":2,"score":0.35989999771118164},{"id":"https://openalex.org/C21001229","wikidata":"https://www.wikidata.org/wiki/Q182868","display_name":"Weather forecasting","level":2,"score":0.35740000009536743},{"id":"https://openalex.org/C103402496","wikidata":"https://www.wikidata.org/wiki/Q1106171","display_name":"Prediction interval","level":2,"score":0.3540000021457672},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.35280001163482666},{"id":"https://openalex.org/C188573790","wikidata":"https://www.wikidata.org/wiki/Q12705","display_name":"Renewable energy","level":2,"score":0.34310001134872437},{"id":"https://openalex.org/C118671147","wikidata":"https://www.wikidata.org/wiki/Q578714","display_name":"Quantile","level":2,"score":0.34130001068115234},{"id":"https://openalex.org/C189119545","wikidata":"https://www.wikidata.org/wiki/Q5128022","display_name":"Probabilistic classification","level":4,"score":0.32359999418258667},{"id":"https://openalex.org/C119898033","wikidata":"https://www.wikidata.org/wiki/Q3433888","display_name":"Ensemble forecasting","level":2,"score":0.31130000948905945},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.30070000886917114},{"id":"https://openalex.org/C120954023","wikidata":"https://www.wikidata.org/wiki/Q1127277","display_name":"Consensus forecast","level":2,"score":0.2890999913215637},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.28209999203681946},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.2791000008583069},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2770000100135803},{"id":"https://openalex.org/C166851805","wikidata":"https://www.wikidata.org/wiki/Q5468165","display_name":"Forecast verification","level":3,"score":0.26649999618530273},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.265500009059906},{"id":"https://openalex.org/C89227174","wikidata":"https://www.wikidata.org/wiki/Q2388981","display_name":"Electric power system","level":3,"score":0.2614000141620636},{"id":"https://openalex.org/C2780150128","wikidata":"https://www.wikidata.org/wiki/Q21948731","display_name":"Extreme learning machine","level":3,"score":0.2605000138282776},{"id":"https://openalex.org/C44492722","wikidata":"https://www.wikidata.org/wiki/Q327069","display_name":"Conditional probability","level":2,"score":0.26030001044273376}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:doi:10.48550/arxiv.2602.13010","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"publisher-specific-oa","license_id":"https://openalex.org/licenses/publisher-specific-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},{"id":"doi:10.48550/arxiv.2602.13010","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.13010","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:doi:10.48550/arxiv.2602.13010","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"publisher-specific-oa","license_id":"https://openalex.org/licenses/publisher-specific-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"sustainable_development_goals":[{"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7","score":0.8898495435714722}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Accurate":[0],"production":[1],"forecasts":[2,26,95,184],"are":[3,75,96],"essential":[4],"to":[5,22,133,138,159,192],"continue":[6],"facilitating":[7],"the":[8,15,88,93,104,122,127,139,143,148,153,161,173,177],"integration":[9],"of":[10,27,38,64,80,168,179,182],"renewable":[11],"energy":[12],"sources":[13],"into":[14],"power":[16,29,105,140,170],"grid.":[17],"This":[18],"paper":[19],"illustrates":[20],"how":[21],"obtain":[23],"probabilistic":[24,52,150,164],"day-ahead":[25],"wind":[28,84,169],"generation":[30],"via":[31],"gradient":[32,58],"boosting":[33,59],"trees":[34],"using":[35,77,102],"an":[36,108,180],"ensemble":[37,181],"weather":[39,183],"forecasts.":[40],"To":[41],"this":[42],"end,":[43],"we":[44],"perform":[45],"a":[46,112],"comparative":[47],"analysis":[48],"across":[49],"three":[50,149],"state-of-the-art":[51],"prediction":[53,151],"methods-conformalised":[54],"quantile":[55],"regression,":[56],"natural":[57],"and":[60,135,142,165],"conditional":[61,154],"diffusion":[62,155],"models-all":[63],"which":[65],"can":[66,185],"be":[67],"combined":[68],"with":[69],"tree-based":[70],"machine":[71,123],"learning.":[72],"The":[73,117],"methods":[74,125],"validated":[76],"four":[78],"years":[79],"data":[81],"for":[82],"all":[83],"farms":[85],"present":[86],"within":[87],"Belgian":[89],"offshore":[90],"zone.":[91],"Additionally,":[92],"point":[94,166,187],"benchmarked":[97],"against":[98],"deterministic":[99],"engineering":[100],"methods,":[101,152],"either":[103],"curve":[106,141],"or":[107],"advanced":[109],"approach":[110],"incorporating":[111],"calibrated":[113,144],"analytical":[114],"wake":[115,145],"model.":[116,146],"experimental":[118],"results":[119],"show":[120],"that":[121,176],"learning":[124],"improve":[126,186],"mean":[128],"absolute":[129],"error":[130],"by":[131,190],"up":[132,191],"53%":[134],"33%":[136],"compared":[137],"Considering":[147],"model":[156],"is":[157],"found":[158],"yield":[160],"best":[162],"overall":[163],"estimate":[167],"generation.":[171],"Moreover,":[172],"findings":[174],"suggest":[175],"use":[178],"forecast":[188],"accuracy":[189],"23%.":[193]},"counts_by_year":[],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2026-02-17T00:00:00"}
