{"id":"https://openalex.org/W2568843976","doi":"https://doi.org/10.1109/icsai.2016.7810951","title":"Research on modeling method describing wind power fluctuation probability density based on nonparametric kernel density estimation","display_name":"Research on modeling method describing wind power fluctuation probability density based on nonparametric kernel density estimation","publication_year":2016,"publication_date":"2016-11-01","ids":{"openalex":"https://openalex.org/W2568843976","doi":"https://doi.org/10.1109/icsai.2016.7810951","mag":"2568843976"},"language":"en","primary_location":{"id":"doi:10.1109/icsai.2016.7810951","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icsai.2016.7810951","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 3rd International Conference on Systems and Informatics (ICSAI)","raw_type":"proceedings-article"},"type":"article","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/A5058103824","display_name":"Daojun Chen","orcid":"https://orcid.org/0000-0003-1654-4152"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Daojun Chen","raw_affiliation_strings":["Hunan Electric Power Corporation Research, Institute of State Grid Company, Changsha, China"],"affiliations":[{"raw_affiliation_string":"Hunan Electric Power Corporation Research, Institute of State Grid Company, Changsha, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101152001","display_name":"Hu Guo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hu Guo","raw_affiliation_strings":["Hunan Electric Power Corporation Research, Institute of State Grid Company, Changsha, China"],"affiliations":[{"raw_affiliation_string":"Hunan Electric Power Corporation Research, Institute of State Grid Company, Changsha, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026676538","display_name":"Jian Zuo","orcid":"https://orcid.org/0000-0002-8279-9666"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jian Zuo","raw_affiliation_strings":["Hunan Electric Power Corporation Research, Institute of State Grid Company, Changsha, China"],"affiliations":[{"raw_affiliation_string":"Hunan Electric Power Corporation Research, Institute of State Grid Company, Changsha, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100328987","display_name":"Xuan Wang","orcid":"https://orcid.org/0000-0002-1122-9818"},"institutions":[{"id":"https://openalex.org/I161350542","display_name":"China Three Gorges University","ror":"https://ror.org/0419nfc77","country_code":"CN","type":"education","lineage":["https://openalex.org/I161350542"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xuan Wang","raw_affiliation_strings":["Hubei Provincial Collaborative Innovation, Center for New Energy Micro-grid China Three Gorges University, Yichang, China"],"affiliations":[{"raw_affiliation_string":"Hubei Provincial Collaborative Innovation, Center for New Energy Micro-grid China Three Gorges University, Yichang, China","institution_ids":["https://openalex.org/I161350542"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5070346662","display_name":"Lei Zhang","orcid":"https://orcid.org/0000-0002-0096-7073"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lei Zhang","raw_affiliation_strings":["Hunan Electric Boiler and Pressure Vessel, Inspection Center Co. Ltd., Changsha, China"],"affiliations":[{"raw_affiliation_string":"Hunan Electric Boiler and Pressure Vessel, Inspection Center Co. Ltd., Changsha, China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5058103824"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.1838,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.60633566,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"5","issue":null,"first_page":"178","last_page":"187"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11052","display_name":"Energy Load and Power Forecasting","score":0.9965000152587891,"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":0.9965000152587891,"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/T14444","display_name":"Power Systems and Renewable Energy","score":0.979200005531311,"subfield":{"id":"https://openalex.org/subfields/2102","display_name":"Energy Engineering and Power Technology"},"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/T11941","display_name":"Power System Reliability and Maintenance","score":0.9567999839782715,"subfield":{"id":"https://openalex.org/subfields/2213","display_name":"Safety, Risk, Reliability and Quality"},"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/kernel-density-estimation","display_name":"Kernel density estimation","score":0.8338932394981384},{"id":"https://openalex.org/keywords/multivariate-kernel-density-estimation","display_name":"Multivariate kernel density estimation","score":0.832797646522522},{"id":"https://openalex.org/keywords/variable-kernel-density-estimation","display_name":"Variable kernel density estimation","score":0.653592586517334},{"id":"https://openalex.org/keywords/nonparametric-statistics","display_name":"Nonparametric statistics","score":0.6409907937049866},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.6030749082565308},{"id":"https://openalex.org/keywords/density-estimation","display_name":"Density estimation","score":0.49380072951316833},{"id":"https://openalex.org/keywords/probability-density-function","display_name":"Probability density function","score":0.4904046058654785},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.46477678418159485},{"id":"https://openalex.org/keywords/spectral-density","display_name":"Spectral density","score":0.4235709607601166},{"id":"https://openalex.org/keywords/bandwidth","display_name":"Bandwidth (computing)","score":0.4234044849872589},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.4149898290634155},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.38753730058670044},{"id":"https://openalex.org/keywords/kernel-method","display_name":"Kernel method","score":0.3822126090526581},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.376711368560791},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.2516005039215088},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.19163626432418823},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.15047132968902588}],"concepts":[{"id":"https://openalex.org/C71134354","wikidata":"https://www.wikidata.org/wiki/Q458825","display_name":"Kernel density estimation","level":3,"score":0.8338932394981384},{"id":"https://openalex.org/C84894716","wikidata":"https://www.wikidata.org/wiki/Q6935135","display_name":"Multivariate kernel density estimation","level":5,"score":0.832797646522522},{"id":"https://openalex.org/C195699287","wikidata":"https://www.wikidata.org/wiki/Q7915722","display_name":"Variable kernel density estimation","level":4,"score":0.653592586517334},{"id":"https://openalex.org/C102366305","wikidata":"https://www.wikidata.org/wiki/Q1097688","display_name":"Nonparametric statistics","level":2,"score":0.6409907937049866},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.6030749082565308},{"id":"https://openalex.org/C189508267","wikidata":"https://www.wikidata.org/wiki/Q17088227","display_name":"Density estimation","level":3,"score":0.49380072951316833},{"id":"https://openalex.org/C197055811","wikidata":"https://www.wikidata.org/wiki/Q207522","display_name":"Probability density function","level":2,"score":0.4904046058654785},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.46477678418159485},{"id":"https://openalex.org/C168110828","wikidata":"https://www.wikidata.org/wiki/Q1331626","display_name":"Spectral density","level":2,"score":0.4235709607601166},{"id":"https://openalex.org/C2776257435","wikidata":"https://www.wikidata.org/wiki/Q1576430","display_name":"Bandwidth (computing)","level":2,"score":0.4234044849872589},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.4149898290634155},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.38753730058670044},{"id":"https://openalex.org/C122280245","wikidata":"https://www.wikidata.org/wiki/Q620622","display_name":"Kernel method","level":3,"score":0.3822126090526581},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.376711368560791},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2516005039215088},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.19163626432418823},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.15047132968902588},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.0},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icsai.2016.7810951","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icsai.2016.7810951","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 3rd International Conference on Systems and Informatics (ICSAI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7","score":0.9100000262260437}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W1554780162","https://openalex.org/W2002474737","https://openalex.org/W2014453842","https://openalex.org/W2017201057","https://openalex.org/W2039607216","https://openalex.org/W2043363014","https://openalex.org/W2043664947","https://openalex.org/W2093522613","https://openalex.org/W2101457442","https://openalex.org/W2103041471","https://openalex.org/W2137295153","https://openalex.org/W2162067990","https://openalex.org/W2165796403","https://openalex.org/W2173314530","https://openalex.org/W2228423595","https://openalex.org/W2349805442","https://openalex.org/W2361092290","https://openalex.org/W2372191501","https://openalex.org/W2382679718","https://openalex.org/W4238717354"],"related_works":["https://openalex.org/W4241010850","https://openalex.org/W3212687977","https://openalex.org/W2583877436","https://openalex.org/W2355371556","https://openalex.org/W3123419490","https://openalex.org/W2144201579","https://openalex.org/W1834385407","https://openalex.org/W2776263260","https://openalex.org/W2026307144","https://openalex.org/W4386285810"],"abstract_inverted_index":{"The":[0],"research":[1],"on":[2,39,64],"the":[3,32,51,96,102,146],"modeling":[4,27,147],"method":[5,28,118],"of":[6,13,54,85],"wind":[7,17,33,55],"power":[8,18,34,56],"fluctuation":[9,35,52],"probability":[10,36],"density":[11,37,42,67,108,141],"is":[12,29,47,62,92,110,133],"great":[14],"importance":[15],"for":[16,31,138],"integration":[19],"and":[20,123,149],"operation.":[21],"In":[22,127],"this":[23,116,136],"paper":[24,137],"a":[25,59,78],"novel":[26],"proposed":[30,134],"based":[38,63],"nonparametric":[40,65,106,139],"kernel":[41,66,107,140],"estimation.":[43,68],"First,":[44],"wavelet":[45],"decomposition":[46],"used":[48],"to":[49,57,94],"extract":[50],"components":[53],"build":[58],"model":[60,81,103],"which":[61,132],"This":[69],"modelling":[70,117],"process":[71],"involves":[72],"bandwidth":[73,79],"optimization.":[74],"Then":[75],"we":[76],"built":[77],"optimization":[80,91],"constrained":[82,89],"by":[83,105,112],"goodness":[84],"fit":[86],"test.":[87],"Finally,":[88],"ordinal":[90],"utilized":[93],"solve":[95],"model.":[97],"Simulation":[98],"results":[99],"show":[100],"that":[101],"constructed":[104],"estimation":[109],"determined":[111],"sample":[113],"data,":[114],"therefore":[115],"features":[119],"with":[120],"higher":[121],"accuracy":[122,148],"more":[124],"general":[125],"applicability.":[126],"addition,":[128],"an":[129],"improved":[130],"strategy,":[131],"in":[135],"estimation,":[142],"also":[143],"greatly":[144],"improves":[145],"computational":[150],"efficiency.":[151]},"counts_by_year":[{"year":2017,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}