{"id":"https://openalex.org/W4312096239","doi":"https://doi.org/10.1109/la-cci54402.2022.9981620","title":"Hybrid Models Based on Error Correction to Predict Thermoelectric Power Plants Fuel Consumption","display_name":"Hybrid Models Based on Error Correction to Predict Thermoelectric Power Plants Fuel Consumption","publication_year":2022,"publication_date":"2022-11-23","ids":{"openalex":"https://openalex.org/W4312096239","doi":"https://doi.org/10.1109/la-cci54402.2022.9981620"},"language":"en","primary_location":{"id":"doi:10.1109/la-cci54402.2022.9981620","is_oa":false,"landing_page_url":"https://doi.org/10.1109/la-cci54402.2022.9981620","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE Latin American Conference on Computational Intelligence (LA-CCI)","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/A5033401347","display_name":"Elias Amancio Siqueira-Filho","orcid":"https://orcid.org/0000-0002-4381-7219"},"institutions":[{"id":"https://openalex.org/I71437568","display_name":"Universidade de Pernambuco","ror":"https://ror.org/00gtcbp88","country_code":"BR","type":"education","lineage":["https://openalex.org/I71437568"]}],"countries":["BR"],"is_corresponding":true,"raw_author_name":"Elias Amancio Siqueira-Filho","raw_affiliation_strings":["University of Pernambuco,Polytechnic School of Pernambuco,Recife,Brazil","Polytechnic School of Pernambuco, University of Pernambuco, Recife, Brazil"],"affiliations":[{"raw_affiliation_string":"University of Pernambuco,Polytechnic School of Pernambuco,Recife,Brazil","institution_ids":["https://openalex.org/I71437568"]},{"raw_affiliation_string":"Polytechnic School of Pernambuco, University of Pernambuco, Recife, Brazil","institution_ids":["https://openalex.org/I71437568"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078944541","display_name":"Maira Farias Andrade Lira","orcid":"https://orcid.org/0000-0002-3839-9681"},"institutions":[{"id":"https://openalex.org/I4210087059","display_name":"Technology Innovation Institute","ror":"https://ror.org/001kv2y39","country_code":"AE","type":"facility","lineage":["https://openalex.org/I4210087059"]},{"id":"https://openalex.org/I4210151455","display_name":"Ministry of Science, Technology and Innovation","ror":"https://ror.org/050zdnc69","country_code":"BR","type":"government","lineage":["https://openalex.org/I4210151455"]}],"countries":["AE","BR"],"is_corresponding":false,"raw_author_name":"Maira Farias Andrade Lira","raw_affiliation_strings":["Advanced Institute of Technology and Innovation,Research Team,Recife,Brazil","Research Team, Advanced Institute of Technology and Innovation, Recife, Brazil"],"affiliations":[{"raw_affiliation_string":"Advanced Institute of Technology and Innovation,Research Team,Recife,Brazil","institution_ids":["https://openalex.org/I4210087059"]},{"raw_affiliation_string":"Research Team, Advanced Institute of Technology and Innovation, Recife, Brazil","institution_ids":["https://openalex.org/I4210151455"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021970061","display_name":"Hugo Valadares Siqueira","orcid":"https://orcid.org/0000-0002-1278-4602"},"institutions":[{"id":"https://openalex.org/I1283613182","display_name":"Universidade Tecnol\u00f3gica Federal do Paran\u00e1","ror":"https://ror.org/002v2kq79","country_code":"BR","type":"education","lineage":["https://openalex.org/I1283613182"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Hugo Valadares Siqueira","raw_affiliation_strings":["Federal University of Technology,Department of Electrical Engineering,Parana,Brazil","Department of Electrical Engineering, Federal University of Technology, Parana, Brazil"],"affiliations":[{"raw_affiliation_string":"Federal University of Technology,Department of Electrical Engineering,Parana,Brazil","institution_ids":["https://openalex.org/I1283613182"]},{"raw_affiliation_string":"Department of Electrical Engineering, Federal University of Technology, Parana, Brazil","institution_ids":["https://openalex.org/I1283613182"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5028876161","display_name":"Carmelo J. A. Bastos-Filho","orcid":"https://orcid.org/0000-0002-0924-5341"},"institutions":[{"id":"https://openalex.org/I71437568","display_name":"Universidade de Pernambuco","ror":"https://ror.org/00gtcbp88","country_code":"BR","type":"education","lineage":["https://openalex.org/I71437568"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Carmelo J. A. Bastos-Filho","raw_affiliation_strings":["University of Pernambuco,Polytechnic School of Pernambuco,Recife,Brazil","Polytechnic School of Pernambuco, University of Pernambuco, Recife, Brazil"],"affiliations":[{"raw_affiliation_string":"University of Pernambuco,Polytechnic School of Pernambuco,Recife,Brazil","institution_ids":["https://openalex.org/I71437568"]},{"raw_affiliation_string":"Polytechnic School of Pernambuco, University of Pernambuco, Recife, Brazil","institution_ids":["https://openalex.org/I71437568"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5033401347"],"corresponding_institution_ids":["https://openalex.org/I71437568"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.1086621,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"51","issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11052","display_name":"Energy Load and Power Forecasting","score":0.9990000128746033,"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":0.9990000128746033,"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/T11326","display_name":"Stock Market Forecasting Methods","score":0.9976999759674072,"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/T11059","display_name":"Market Dynamics and Volatility","score":0.9790999889373779,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/autoregressive-integrated-moving-average","display_name":"Autoregressive integrated moving average","score":0.655783474445343},{"id":"https://openalex.org/keywords/fuel-efficiency","display_name":"Fuel efficiency","score":0.6362125277519226},{"id":"https://openalex.org/keywords/particle-swarm-optimization","display_name":"Particle swarm optimization","score":0.6130926609039307},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5386936068534851},{"id":"https://openalex.org/keywords/diesel-fuel","display_name":"Diesel fuel","score":0.5187833309173584},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5101840496063232},{"id":"https://openalex.org/keywords/multilayer-perceptron","display_name":"Multilayer perceptron","score":0.5030307173728943},{"id":"https://openalex.org/keywords/energy-consumption","display_name":"Energy consumption","score":0.41170066595077515},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.3730921149253845},{"id":"https://openalex.org/keywords/automotive-engineering","display_name":"Automotive engineering","score":0.36853307485580444},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.31291618943214417},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.1604292392730713},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.1505095660686493},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.09992104768753052}],"concepts":[{"id":"https://openalex.org/C24338571","wikidata":"https://www.wikidata.org/wiki/Q2566298","display_name":"Autoregressive integrated moving average","level":3,"score":0.655783474445343},{"id":"https://openalex.org/C45882903","wikidata":"https://www.wikidata.org/wiki/Q5042317","display_name":"Fuel efficiency","level":2,"score":0.6362125277519226},{"id":"https://openalex.org/C85617194","wikidata":"https://www.wikidata.org/wiki/Q2072794","display_name":"Particle swarm optimization","level":2,"score":0.6130926609039307},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5386936068534851},{"id":"https://openalex.org/C138171918","wikidata":"https://www.wikidata.org/wiki/Q38423","display_name":"Diesel fuel","level":2,"score":0.5187833309173584},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5101840496063232},{"id":"https://openalex.org/C179717631","wikidata":"https://www.wikidata.org/wiki/Q2991667","display_name":"Multilayer perceptron","level":3,"score":0.5030307173728943},{"id":"https://openalex.org/C2780165032","wikidata":"https://www.wikidata.org/wiki/Q16869822","display_name":"Energy consumption","level":2,"score":0.41170066595077515},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.3730921149253845},{"id":"https://openalex.org/C171146098","wikidata":"https://www.wikidata.org/wiki/Q124192","display_name":"Automotive engineering","level":1,"score":0.36853307485580444},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.31291618943214417},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.1604292392730713},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.1505095660686493},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.09992104768753052},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/la-cci54402.2022.9981620","is_oa":false,"landing_page_url":"https://doi.org/10.1109/la-cci54402.2022.9981620","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE Latin American Conference on Computational Intelligence (LA-CCI)","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":0.8600000143051147}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W1586335931","https://openalex.org/W1973002019","https://openalex.org/W1989272293","https://openalex.org/W2015755712","https://openalex.org/W2019319598","https://openalex.org/W2024760831","https://openalex.org/W2116512828","https://openalex.org/W2117014758","https://openalex.org/W2168141462","https://openalex.org/W2598609792","https://openalex.org/W2604055951","https://openalex.org/W2786343517","https://openalex.org/W2975813758","https://openalex.org/W2995923051","https://openalex.org/W3013638355","https://openalex.org/W3025543387","https://openalex.org/W3050886486","https://openalex.org/W3082366482","https://openalex.org/W3096997982","https://openalex.org/W3109212207","https://openalex.org/W3204530799","https://openalex.org/W3214016374","https://openalex.org/W6777298427"],"related_works":["https://openalex.org/W3175321409","https://openalex.org/W4312561791","https://openalex.org/W2389894046","https://openalex.org/W2215717369","https://openalex.org/W4312309719","https://openalex.org/W4391216528","https://openalex.org/W2980748541","https://openalex.org/W4399581288","https://openalex.org/W4313123484","https://openalex.org/W2146461990"],"abstract_inverted_index":{"Monitoring":[0],"and":[1,40,70,79,104,140,173],"controlling":[2],"operating":[3,30],"parameters":[4,31],"in":[5,48,97,161],"thermoelectric":[6],"power":[7],"plants":[8],"(TPPs)":[9],"is":[10,32,183],"a":[11,41,49,98,180],"relevant":[12],"activity":[13],"to":[14,24,60,84,93,125],"achieve":[15],"the":[16,20,27,62,86,127,145,153],"optimal":[17],"functioning":[18],"of":[19,29,65,89,132,176],"electric":[21],"system.":[22],"Due":[23],"system":[25],"complexity,":[26],"adjustment":[28],"often":[33],"performed":[34],"based":[35],"on":[36],"technical":[37],"team":[38],"know-how":[39],"simplified":[42],"analytical":[43],"model,":[44],"which":[45],"can":[46,164],"result":[47],"suboptimal":[50],"regime.":[51],"Therefore,":[52],"time":[53,128],"series":[54],"forecasting":[55],"methods":[56],"are":[57,82],"promising":[58],"alternatives":[59],"describe":[61],"observed":[63],"behavior":[64],"energy":[66],"generation,":[67],"fuel":[68,90,106],"consumption,":[69],"greenhouse":[71],"gas":[72],"emissions.":[73],"For":[74],"this":[75,162],"purpose,":[76],"different":[77],"linear":[78,117,155],"non-linear":[80],"models":[81,118],"applied":[83],"predict":[85,126],"next":[87],"minute":[88],"consumption":[91],"related":[92],"an":[94,168],"engine":[95],"used":[96,166],"Brazilian":[99],"TPP":[100],"powered":[101],"by":[102,135,148],"diesel":[103],"heavy":[105],"oil":[107],"(HFO).":[108],"This":[109],"work":[110,163],"uses":[111],"error":[112,146],"correction-based":[113],"approaches":[114],"that":[115],"address":[116],"combined":[119],"with":[120,152],"Artificial":[121],"Neural":[122],"Networks":[123],"(ANN)":[124],"series.":[129],"The":[130,157],"combination":[131],"ARIMA":[133],"optimized":[134],"Particle":[136],"Swarm":[137],"Optimization":[138],"(PSO)":[139],"Multilayer":[141],"Perceptron":[142],"(MLP)":[143],"reduced":[144],"rate":[147],"32.97%":[149],"when":[150,179],"compared":[151],"best":[154],"model.":[156],"hybrid":[158],"model":[159],"developed":[160],"be":[165],"as":[167],"auxiliary":[169],"tool":[170],"for":[171],"maintenance":[172],"operation":[174],"planning":[175],"TPPs":[177],"engines":[178],"short-term":[181],"horizon":[182],"considered.":[184]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}