{"id":"https://openalex.org/W2601642459","doi":"https://doi.org/10.1109/la-cci.2016.7885697","title":"A hybrid method using Exponential Smoothing and Gradient Boosting for electrical short-term load forecasting","display_name":"A hybrid method using Exponential Smoothing and Gradient Boosting for electrical short-term load forecasting","publication_year":2016,"publication_date":"2016-11-01","ids":{"openalex":"https://openalex.org/W2601642459","doi":"https://doi.org/10.1109/la-cci.2016.7885697","mag":"2601642459"},"language":"en","primary_location":{"id":"doi:10.1109/la-cci.2016.7885697","is_oa":false,"landing_page_url":"https://doi.org/10.1109/la-cci.2016.7885697","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 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/A5068259340","display_name":"Victor Mayrink","orcid":null},"institutions":[{"id":"https://openalex.org/I101100930","display_name":"Universidade Federal de Juiz de Fora","ror":"https://ror.org/04yqw9c44","country_code":"BR","type":"education","lineage":["https://openalex.org/I101100930"]}],"countries":["BR"],"is_corresponding":true,"raw_author_name":"Victor Mayrink","raw_affiliation_strings":["Universidade Federal de Juiz de Fora"],"affiliations":[{"raw_affiliation_string":"Universidade Federal de Juiz de Fora","institution_ids":["https://openalex.org/I101100930"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5042793371","display_name":"Henrique S. Hippert","orcid":null},"institutions":[{"id":"https://openalex.org/I101100930","display_name":"Universidade Federal de Juiz de Fora","ror":"https://ror.org/04yqw9c44","country_code":"BR","type":"education","lineage":["https://openalex.org/I101100930"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Henrique S. Hippert","raw_affiliation_strings":["Universidade Federal de Juiz de Fora"],"affiliations":[{"raw_affiliation_string":"Universidade Federal de Juiz de Fora","institution_ids":["https://openalex.org/I101100930"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5068259340"],"corresponding_institution_ids":["https://openalex.org/I101100930"],"apc_list":null,"apc_paid":null,"fwci":1.1026,"has_fulltext":false,"cited_by_count":34,"citation_normalized_percentile":{"value":0.81322781,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":99},"biblio":{"volume":null,"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":0.9998999834060669,"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":0.9998999834060669,"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":0.9945999979972839,"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.9941999912261963,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/exponential-smoothing","display_name":"Exponential smoothing","score":0.9628938436508179},{"id":"https://openalex.org/keywords/gradient-boosting","display_name":"Gradient boosting","score":0.6959695816040039},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6741809844970703},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.6196659207344055},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.6176842451095581},{"id":"https://openalex.org/keywords/smoothing","display_name":"Smoothing","score":0.5974741578102112},{"id":"https://openalex.org/keywords/exponential-function","display_name":"Exponential function","score":0.5308467149734497},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4460415244102478},{"id":"https://openalex.org/keywords/energy","display_name":"Energy (signal processing)","score":0.43550407886505127},{"id":"https://openalex.org/keywords/exponential-growth","display_name":"Exponential growth","score":0.42722076177597046},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3996722996234894},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.35138431191444397},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.175197571516037},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.13414284586906433}],"concepts":[{"id":"https://openalex.org/C133710760","wikidata":"https://www.wikidata.org/wiki/Q775837","display_name":"Exponential smoothing","level":2,"score":0.9628938436508179},{"id":"https://openalex.org/C70153297","wikidata":"https://www.wikidata.org/wiki/Q5591907","display_name":"Gradient boosting","level":3,"score":0.6959695816040039},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6741809844970703},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.6196659207344055},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.6176842451095581},{"id":"https://openalex.org/C3770464","wikidata":"https://www.wikidata.org/wiki/Q775963","display_name":"Smoothing","level":2,"score":0.5974741578102112},{"id":"https://openalex.org/C151376022","wikidata":"https://www.wikidata.org/wiki/Q168698","display_name":"Exponential function","level":2,"score":0.5308467149734497},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4460415244102478},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.43550407886505127},{"id":"https://openalex.org/C75235859","wikidata":"https://www.wikidata.org/wiki/Q582659","display_name":"Exponential growth","level":2,"score":0.42722076177597046},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3996722996234894},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.35138431191444397},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.175197571516037},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.13414284586906433},{"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/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/la-cci.2016.7885697","is_oa":false,"landing_page_url":"https://doi.org/10.1109/la-cci.2016.7885697","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 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","score":0.8999999761581421,"id":"https://metadata.un.org/sdg/7"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W1594031697","https://openalex.org/W1678356000","https://openalex.org/W1984931792","https://openalex.org/W1987552279","https://openalex.org/W1988790447","https://openalex.org/W1996730695","https://openalex.org/W2000359198","https://openalex.org/W2016023958","https://openalex.org/W2021796589","https://openalex.org/W2038594239","https://openalex.org/W2046185460","https://openalex.org/W2081770709","https://openalex.org/W2098207764","https://openalex.org/W2126603600","https://openalex.org/W2145073242","https://openalex.org/W2145732764","https://openalex.org/W2151767444","https://openalex.org/W2156636680","https://openalex.org/W2811507150","https://openalex.org/W3126081667","https://openalex.org/W6681651645"],"related_works":["https://openalex.org/W2093101924","https://openalex.org/W2967733078","https://openalex.org/W3204430031","https://openalex.org/W3137904399","https://openalex.org/W4310492845","https://openalex.org/W2885778889","https://openalex.org/W4310224730","https://openalex.org/W2766514146","https://openalex.org/W4289703016","https://openalex.org/W2885516856"],"abstract_inverted_index":{"Accurate":[0],"load":[1,21],"forecasts":[2],"are":[3],"required":[4],"in":[5,57],"most":[6],"tasks":[7],"of":[8],"energy":[9],"planning.":[10],"In":[11],"this":[12],"paper":[13],"we":[14],"present":[15],"a":[16,27,37,54],"hybrid":[17],"method":[18,29],"for":[19,30],"short-term":[20],"forecasting.":[22],"We":[23],"combined":[24],"Exponential":[25],"Smoothing,":[26],"classical":[28],"time":[31],"series":[32],"forecasting,":[33],"with":[34,47],"Gradient":[35],"Boosting,":[36],"powerful":[38],"machine":[39],"learning":[40],"algorithm.":[41],"The":[42],"proposed":[43],"model":[44],"was":[45],"tested":[46],"real":[48],"data":[49],"and":[50],"the":[51],"results":[52],"showed":[53],"considerable":[55],"improvement":[56],"forecasting":[58],"accuracy.":[59]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":9},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":6},{"year":2020,"cited_by_count":5},{"year":2019,"cited_by_count":3},{"year":2018,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
