{"id":"https://openalex.org/W3082874580","doi":"https://doi.org/10.1109/fuzz48607.2020.9177767","title":"Solar Energy Forecasting With Fuzzy Time Series Using High-Order Fuzzy Cognitive Maps","display_name":"Solar Energy Forecasting With Fuzzy Time Series Using High-Order Fuzzy Cognitive Maps","publication_year":2020,"publication_date":"2020-07-01","ids":{"openalex":"https://openalex.org/W3082874580","doi":"https://doi.org/10.1109/fuzz48607.2020.9177767","mag":"3082874580"},"language":"en","primary_location":{"id":"doi:10.1109/fuzz48607.2020.9177767","is_oa":false,"landing_page_url":"https://doi.org/10.1109/fuzz48607.2020.9177767","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","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/A5038448571","display_name":"Omid Orang","orcid":"https://orcid.org/0000-0002-4077-3775"},"institutions":[{"id":"https://openalex.org/I110200422","display_name":"Universidade Federal de Minas Gerais","ror":"https://ror.org/0176yjw32","country_code":"BR","type":"education","lineage":["https://openalex.org/I110200422"]}],"countries":["BR"],"is_corresponding":true,"raw_author_name":"Omid Orang","raw_affiliation_strings":["Machine Intelligence and Data Science (MINDS) Lab, Universidade Federal de Minas Gerais, UFMG, Belo Horizonte, Brazil"],"affiliations":[{"raw_affiliation_string":"Machine Intelligence and Data Science (MINDS) Lab, Universidade Federal de Minas Gerais, UFMG, Belo Horizonte, Brazil","institution_ids":["https://openalex.org/I110200422"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055102966","display_name":"Rodrigo Silva","orcid":"https://orcid.org/0000-0003-2547-3835"},"institutions":[{"id":"https://openalex.org/I10824318","display_name":"Universidade Federal de Ouro Preto","ror":"https://ror.org/056s65p46","country_code":"BR","type":"education","lineage":["https://openalex.org/I10824318"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Rodrigo Silva","raw_affiliation_strings":["Department of Computer Science, Universidade Federal de Ouro Preto, UFOP, Ouro Preto, Brazil"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Universidade Federal de Ouro Preto, UFOP, Ouro Preto, Brazil","institution_ids":["https://openalex.org/I10824318"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069331818","display_name":"Petr\u00f4nio C\u00e2ndido de Lima e Silva","orcid":"https://orcid.org/0000-0002-1202-2552"},"institutions":[{"id":"https://openalex.org/I4210143109","display_name":"Instituto Federal de Educa\u00e7\u00e3o Ci\u00eancia e Tecnologia do Norte de Minas Gerais","ror":"https://ror.org/03w6rv149","country_code":"BR","type":"education","lineage":["https://openalex.org/I4210143109"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Petronio Candido de Lima e Silva","raw_affiliation_strings":["Instituto Federal do Norte de Minas Gerais, IFNMG, Januaria, Brazil"],"affiliations":[{"raw_affiliation_string":"Instituto Federal do Norte de Minas Gerais, IFNMG, Januaria, Brazil","institution_ids":["https://openalex.org/I4210143109"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5053773659","display_name":"Frederico Gadelha Guimar\u00e3es","orcid":"https://orcid.org/0000-0001-9238-8839"},"institutions":[{"id":"https://openalex.org/I110200422","display_name":"Universidade Federal de Minas Gerais","ror":"https://ror.org/0176yjw32","country_code":"BR","type":"education","lineage":["https://openalex.org/I110200422"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Frederico Gadelha Guimaraes","raw_affiliation_strings":["Machine Intelligence and Data Science (MINDS) Lab, Universidade Federal de Minas Gerais, UFMG, Belo Horizonte, Brazil"],"affiliations":[{"raw_affiliation_string":"Machine Intelligence and Data Science (MINDS) Lab, Universidade Federal de Minas Gerais, UFMG, Belo Horizonte, Brazil","institution_ids":["https://openalex.org/I110200422"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5038448571"],"corresponding_institution_ids":["https://openalex.org/I110200422"],"apc_list":null,"apc_paid":null,"fwci":0.7954,"has_fulltext":false,"cited_by_count":21,"citation_normalized_percentile":{"value":0.78493517,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12805","display_name":"Cognitive Science and Mapping","score":0.9968000054359436,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12805","display_name":"Cognitive Science and Mapping","score":0.9968000054359436,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.9815999865531921,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T12368","display_name":"Grey System Theory Applications","score":0.953000009059906,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/fuzzy-cognitive-map","display_name":"Fuzzy cognitive map","score":0.709041953086853},{"id":"https://openalex.org/keywords/fuzzy-logic","display_name":"Fuzzy logic","score":0.5805299282073975},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5632629990577698},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.5123735666275024},{"id":"https://openalex.org/keywords/probabilistic-forecasting","display_name":"Probabilistic forecasting","score":0.4980597496032715},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4964748024940491},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4718375504016876},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.47157591581344604},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.4628993272781372},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.38457217812538147},{"id":"https://openalex.org/keywords/fuzzy-number","display_name":"Fuzzy number","score":0.3558833599090576},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.32554003596305847},{"id":"https://openalex.org/keywords/fuzzy-set","display_name":"Fuzzy set","score":0.30863308906555176}],"concepts":[{"id":"https://openalex.org/C5041914","wikidata":"https://www.wikidata.org/wiki/Q5511107","display_name":"Fuzzy cognitive map","level":5,"score":0.709041953086853},{"id":"https://openalex.org/C58166","wikidata":"https://www.wikidata.org/wiki/Q224821","display_name":"Fuzzy logic","level":2,"score":0.5805299282073975},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5632629990577698},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.5123735666275024},{"id":"https://openalex.org/C122282355","wikidata":"https://www.wikidata.org/wiki/Q7246855","display_name":"Probabilistic forecasting","level":3,"score":0.4980597496032715},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4964748024940491},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4718375504016876},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.47157591581344604},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.4628993272781372},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.38457217812538147},{"id":"https://openalex.org/C1883856","wikidata":"https://www.wikidata.org/wiki/Q3407463","display_name":"Fuzzy number","level":4,"score":0.3558833599090576},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.32554003596305847},{"id":"https://openalex.org/C42011625","wikidata":"https://www.wikidata.org/wiki/Q1055058","display_name":"Fuzzy set","level":3,"score":0.30863308906555176},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/fuzz48607.2020.9177767","is_oa":false,"landing_page_url":"https://doi.org/10.1109/fuzz48607.2020.9177767","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Affordable and clean energy","score":0.8799999952316284,"id":"https://metadata.un.org/sdg/7"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":47,"referenced_works":["https://openalex.org/W1125761772","https://openalex.org/W1576986334","https://openalex.org/W1966153007","https://openalex.org/W1971869067","https://openalex.org/W1992096620","https://openalex.org/W1998382295","https://openalex.org/W2023964456","https://openalex.org/W2025426031","https://openalex.org/W2029074121","https://openalex.org/W2031404163","https://openalex.org/W2049995592","https://openalex.org/W2053571968","https://openalex.org/W2069178824","https://openalex.org/W2069454342","https://openalex.org/W2085752583","https://openalex.org/W2089082497","https://openalex.org/W2092261825","https://openalex.org/W2095224843","https://openalex.org/W2131453387","https://openalex.org/W2137947700","https://openalex.org/W2150755414","https://openalex.org/W2153733695","https://openalex.org/W2212485538","https://openalex.org/W2571945220","https://openalex.org/W2606111292","https://openalex.org/W2747035712","https://openalex.org/W2748669035","https://openalex.org/W2770882920","https://openalex.org/W2785935768","https://openalex.org/W2809282474","https://openalex.org/W2811272056","https://openalex.org/W2896327000","https://openalex.org/W2902876966","https://openalex.org/W2912565176","https://openalex.org/W2923573337","https://openalex.org/W2944902768","https://openalex.org/W2951442608","https://openalex.org/W2989616544","https://openalex.org/W3012383881","https://openalex.org/W3016369142","https://openalex.org/W4211007335","https://openalex.org/W4241443503","https://openalex.org/W4288257356","https://openalex.org/W6649967988","https://openalex.org/W6680895833","https://openalex.org/W6756996579","https://openalex.org/W6912399606"],"related_works":["https://openalex.org/W2063798559","https://openalex.org/W2991207020","https://openalex.org/W2747524643","https://openalex.org/W1486684451","https://openalex.org/W2116249946","https://openalex.org/W2801459815","https://openalex.org/W2099285375","https://openalex.org/W4361273540","https://openalex.org/W4361984403","https://openalex.org/W4317826560"],"abstract_inverted_index":{"Various":[0],"studies":[1],"indicate":[2],"that":[3,104,187],"Fuzzy":[4,38,65,72],"Time":[5,73],"Series":[6,74],"(FTS)":[7],"methods":[8,22],"can":[9],"obtain":[10],"high":[11],"accuracy":[12,170],"in":[13,27,47,113],"a":[14,119,198],"variety":[15],"of":[16,37,96,116,164,171,178,201],"forecasting":[17,54,126],"applciations.":[18],"However,":[19],"weighted":[20],"FTS":[21,53,150,155],"tend":[23],"to":[24,29,43,99,138,192],"show":[25,186],"superiority":[26],"contrast":[28],"weightless":[30],"ones.":[31],"This":[32],"study":[33],"exploits":[34],"the":[35,45,48,52,77,82,88,92,101,106,110,114,135,162,169,172,176,188,194],"use":[36],"Cognitive":[39,66],"Map":[40],"(FCM)":[41],"technique":[42],"generate":[44],"rules":[46],"knowledge":[49],"base":[50],"for":[51,130],"method.":[55],"The":[56,94,140,158,184],"proposed":[57,141],"hybrid":[58],"method,":[59],"named":[60],"HFCM-FTS,":[61],"combines":[62],"High":[63,70,148],"Order":[64,71,149],"Maps":[67],"(HFCM)":[68],"and":[69,152,182],"(HOFTS),":[75],"where":[76],"weight":[78,102],"matrices":[79,103],"associated":[80],"with":[81,127,145,197],"state":[83],"transitions":[84],"are":[85],"learned":[86],"via":[87],"genetic":[89],"algorithm":[90],"from":[91,134],"data.":[93],"objective":[95],"FCM":[97],"is":[98,143,190],"find":[100],"model":[105,174],"causal":[107],"relations":[108],"among":[109],"concepts":[111],"defined":[112],"Universe":[115],"Discourse.":[117],"As":[118],"case":[120],"study,":[121],"we":[122],"consider":[123],"solar":[124,132],"energy":[125],"public":[128],"data":[129],"Brazilian":[131],"stations":[133],"year":[136],"2012":[137],"2015.":[139],"HFCM-FTS":[142,189],"compared":[144],"HOFTS,":[146],"Weighted":[147,154],"(WHOFTS),":[151],"Probabilistic":[153],"(PWFTS)":[156],"methods.":[157],"experiments":[159],"also":[160],"cover":[161],"influence":[163],"three":[165],"modeling":[166],"elements":[167],"on":[168],"presented":[173],"including":[175],"number":[177,200],"concepts,":[179],"activation":[180],"function,":[181],"bias.":[183],"results":[185,196],"able":[191],"achieve":[193],"best":[195],"low":[199],"concepts.":[202]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":7},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":3},{"year":2020,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
