乐胖代购免代理版

{"id":"https://openalex.org/W4388092686","doi":"https://doi.org/10.48550/arxiv.2310.18629","title":"Explainable Modeling for Wind Power Forecasting: A Glass-Box Approach with Exceptional Accuracy","display_name":"Explainable Modeling for Wind Power Forecasting: A Glass-Box Approach with Exceptional Accuracy","publication_year":2023,"publication_date":"2023-01-01","ids":{"openalex":"https://openalex.org/W4388092686","doi":"https://doi.org/10.48550/arxiv.2310.18629"},"language":"en","primary_location":{"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2310.18629","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":["Cornell University"],"type":"repository"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false},"type":"preprint","type_crossref":"posted-content","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/abs/2310.18629","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5102868046","display_name":"Wenlong Liao","orcid":"https://orcid.org/0000-0003-1175-0280"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liao, Wenlong","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089732960","display_name":"Fernando Port\u00e9\u2010Agel","orcid":"https://orcid.org/0000-0002-9913-3350"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Port\u00e9-Agel, Fernando","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074029890","display_name":"Jiannong Fang","orcid":"https://orcid.org/0000-0002-9205-6444"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fang, Jiannong","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5046077655","display_name":"Birgitte Bak\u2010Jensen","orcid":"https://orcid.org/0000-0001-8271-1356"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bak-Jensen, Birgitte","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004292099","display_name":"Guangchun Ruan","orcid":"https://orcid.org/0000-0003-2660-9298"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ruan, Guangchun","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5024859559","display_name":"Zhe Yang","orcid":"https://orcid.org/0000-0002-7018-0823"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Zhe","raw_affiliation_strings":[],"affiliations":[]}],"institution_assertions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.84258,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":67,"max":78},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T11052","display_name":"Energy Load and Power Forecasting","score":0.9988,"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":0.9988,"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/T10424","display_name":"Electric Power System Optimization","score":0.9336,"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/interpretability","display_name":"Interpretability","score":0.81198514},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.62941873},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting","score":0.56136346},{"id":"https://openalex.org/keywords/black-box","display_name":"Black box","score":0.4713922},{"id":"https://openalex.org/keywords/white-box","display_name":"White box","score":0.43969098},{"id":"https://openalex.org/keywords/gradient-boosting","display_name":"Gradient boosting","score":0.431828},{"id":"https://openalex.org/keywords/wind-power-forecasting","display_name":"Wind Power Forecasting","score":0.4180353}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.81198514},{"id":"https://openalex.org/C78600449","wikidata":"https://www.wikidata.org/wiki/Q43302","display_name":"Wind power","level":2,"score":0.6853566},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6469374},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.62941873},{"id":"https://openalex.org/C2780233690","wikidata":"https://www.wikidata.org/wiki/Q535347","display_name":"Transparency (behavior)","level":2,"score":0.59024906},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5802726},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.56136346},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5121243},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.50478995},{"id":"https://openalex.org/C94966114","wikidata":"https://www.wikidata.org/wiki/Q29256","display_name":"Black box","level":2,"score":0.4713922},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.45102638},{"id":"https://openalex.org/C180932941","wikidata":"https://www.wikidata.org/wiki/Q997233","display_name":"White box","level":2,"score":0.43969098},{"id":"https://openalex.org/C70153297","wikidata":"https://www.wikidata.org/wiki/Q5591907","display_name":"Gradient boosting","level":3,"score":0.431828},{"id":"https://openalex.org/C2781084341","wikidata":"https://www.wikidata.org/wiki/Q2583670","display_name":"Wind power forecasting","level":4,"score":0.4180353},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.40507132},{"id":"https://openalex.org/C89227174","wikidata":"https://www.wikidata.org/wiki/Q2388981","display_name":"Electric power system","level":3,"score":0.3780951},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.20849273},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.17518172},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","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/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2310.18629","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":["Cornell University"],"type":"repository"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false},{"is_oa":false,"landing_page_url":"https://api.datacite.org/dois/10.48550/arxiv.2310.18629","pdf_url":null,"source":{"id":"https://openalex.org/S4393179698","display_name":"DataCite API","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210145204","host_organization_name":"DataCite","host_organization_lineage":["https://openalex.org/I4210145204"],"host_organization_lineage_names":["DataCite"],"type":"metadata"},"license":null,"license_id":null,"version":null}],"best_oa_location":{"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2310.18629","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":["Cornell University"],"type":"repository"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false},"sustainable_development_goals":[{"display_name":"Affordable and clean energy","score":0.91,"id":"https://metadata.un.org/sdg/7"}],"grants":[],"datasets":[],"versions":[],"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4387589990","https://openalex.org/W4297660007","https://openalex.org/W4283692230","https://openalex.org/W3203722024","https://openalex.org/W2998015774","https://openalex.org/W2943982549","https://openalex.org/W2886918272","https://openalex.org/W2797441709","https://openalex.org/W2346578521","https://openalex.org/W2047881532"],"abstract_inverted_index":{"Machine":[0],"learning":[1],"models":[2,125],"(e.g.,":[3,49],"neural":[4,133],"networks)":[5],"achieve":[6],"high":[7,141],"accuracy":[8,37,142],"in":[9],"wind":[10,41,72,111,154],"power":[11,42,73,112,155],"forecasting,":[12],"but":[13],"they":[14],"are":[15,52],"usually":[16],"regarded":[17],"as":[18,148],"black":[19],"boxes":[20],"that":[21,34,88,101],"lack":[22],"interpretability.":[23],"To":[24],"address":[25],"this":[26],"issue,":[27],"the":[28,60,67,79,95,102,108,131,144],"paper":[29],"proposes":[30],"a":[31,149],"glass-box":[32,104,146],"approach":[33,105,147],"combines":[35],"exceptional":[36],"with":[38],"transparency":[39,139],"for":[40,152],"forecasting.":[43,156],"Specifically,":[44],"advanced":[45],"artificial":[46],"intelligence":[47],"methods":[48],"gradient":[50],"boosting)":[51],"innovatively":[53],"employed":[54],"to":[55,130],"create":[56],"shape":[57],"functions":[58,64],"within":[59],"forecasting":[61,80,113],"model.":[62],"These":[63],"effectively":[65,106],"map":[66],"intricate":[68],"non-linear":[69],"relationships":[70],"between":[71],"output":[74],"and":[75,92,117,126,140],"input":[76,96],"features.":[77,97],"Furthermore,":[78],"model":[81],"is":[82],"enriched":[83],"by":[84],"incorporating":[85],"interaction":[86],"terms":[87],"adeptly":[89],"capture":[90],"interdependencies":[91],"synergies":[93],"among":[94],"Simulation":[98],"results":[99,109],"show":[100],"proposed":[103,145],"interprets":[107],"of":[110,138],"from":[114],"both":[115],"global":[116],"instance":[118],"perspectives.":[119],"Besides,":[120],"it":[121],"outperforms":[122],"most":[123],"benchmark":[124],"exhibits":[127],"comparable":[128],"performance":[129],"best-performing":[132],"networks.":[134],"This":[135],"dual":[136],"strength":[137],"positions":[143],"compelling":[150],"choice":[151],"reliable":[153]},"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W4388092686","counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2025-01-02T07:01:49.930658","created_date":"2023-11-01"}