{"id":"https://openalex.org/W2570685097","doi":"https://doi.org/10.1109/iecon.2016.7793935","title":"Estimation of temperature correlation with household electricity demand for forecasting application","display_name":"Estimation of temperature correlation with household electricity demand for forecasting application","publication_year":2016,"publication_date":"2016-10-01","ids":{"openalex":"https://openalex.org/W2570685097","doi":"https://doi.org/10.1109/iecon.2016.7793935","mag":"2570685097"},"language":"en","primary_location":{"id":"doi:10.1109/iecon.2016.7793935","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iecon.2016.7793935","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society","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/A5026016939","display_name":"Fatima Amara","orcid":"https://orcid.org/0000-0003-0249-5775"},"institutions":[{"id":"https://openalex.org/I63341726","display_name":"Universit\u00e9 du Qu\u00e9bec \u00e0 Trois-Rivi\u00e8res","ror":"https://ror.org/02xrw9r68","country_code":"CA","type":"education","lineage":["https://openalex.org/I49663120","https://openalex.org/I63341726"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Fatima Amara","raw_affiliation_strings":["Hydrogen Research Institute, Universit\u00e9 du Qu\u00e9bec \u00e0 Trois-Rivi\u00e8res, Trois-Rivi\u00e8res, Qu\u00e9bec, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hydrogen Research Institute, Universit\u00e9 du Qu\u00e9bec \u00e0 Trois-Rivi\u00e8res, Trois-Rivi\u00e8res, Qu\u00e9bec, Canada","institution_ids":["https://openalex.org/I63341726"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008090389","display_name":"Kodjo Agbossou","orcid":"https://orcid.org/0000-0003-1441-424X"},"institutions":[{"id":"https://openalex.org/I63341726","display_name":"Universit\u00e9 du Qu\u00e9bec \u00e0 Trois-Rivi\u00e8res","ror":"https://ror.org/02xrw9r68","country_code":"CA","type":"education","lineage":["https://openalex.org/I49663120","https://openalex.org/I63341726"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Kodjo Agbossou","raw_affiliation_strings":["Hydrogen Research Institute, Universit\u00e9 du Qu\u00e9bec \u00e0 Trois-Rivi\u00e8res, Trois-Rivi\u00e8res, Qu\u00e9bec, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hydrogen Research Institute, Universit\u00e9 du Qu\u00e9bec \u00e0 Trois-Rivi\u00e8res, Trois-Rivi\u00e8res, Qu\u00e9bec, Canada","institution_ids":["https://openalex.org/I63341726"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083000817","display_name":"Yves Dub\u00e9","orcid":null},"institutions":[{"id":"https://openalex.org/I63341726","display_name":"Universit\u00e9 du Qu\u00e9bec \u00e0 Trois-Rivi\u00e8res","ror":"https://ror.org/02xrw9r68","country_code":"CA","type":"education","lineage":["https://openalex.org/I49663120","https://openalex.org/I63341726"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Yves Dube","raw_affiliation_strings":["Hydrogen Research Institute, Universit\u00e9 du Qu\u00e9bec \u00e0 Trois-Rivi\u00e8res, Trois-Rivi\u00e8res, Qu\u00e9bec, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hydrogen Research Institute, Universit\u00e9 du Qu\u00e9bec \u00e0 Trois-Rivi\u00e8res, Trois-Rivi\u00e8res, Qu\u00e9bec, Canada","institution_ids":["https://openalex.org/I63341726"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043426685","display_name":"Sousso K\u00e9louwani","orcid":"https://orcid.org/0000-0002-1163-9147"},"institutions":[{"id":"https://openalex.org/I63341726","display_name":"Universit\u00e9 du Qu\u00e9bec \u00e0 Trois-Rivi\u00e8res","ror":"https://ror.org/02xrw9r68","country_code":"CA","type":"education","lineage":["https://openalex.org/I49663120","https://openalex.org/I63341726"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Sousso Kelouwani","raw_affiliation_strings":["Hydrogen Research Institute, Universit\u00e9 du Qu\u00e9bec \u00e0 Trois-Rivi\u00e8res, Trois-Rivi\u00e8res, Qu\u00e9bec, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hydrogen Research Institute, Universit\u00e9 du Qu\u00e9bec \u00e0 Trois-Rivi\u00e8res, Trois-Rivi\u00e8res, Qu\u00e9bec, Canada","institution_ids":["https://openalex.org/I63341726"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5026730481","display_name":"Alben Cardenas","orcid":"https://orcid.org/0000-0001-9163-4989"},"institutions":[{"id":"https://openalex.org/I63341726","display_name":"Universit\u00e9 du Qu\u00e9bec \u00e0 Trois-Rivi\u00e8res","ror":"https://ror.org/02xrw9r68","country_code":"CA","type":"education","lineage":["https://openalex.org/I49663120","https://openalex.org/I63341726"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Alben Cardenas","raw_affiliation_strings":["Hydrogen Research Institute, Universit\u00e9 du Qu\u00e9bec \u00e0 Trois-Rivi\u00e8res, Trois-Rivi\u00e8res, Qu\u00e9bec, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hydrogen Research Institute, Universit\u00e9 du Qu\u00e9bec \u00e0 Trois-Rivi\u00e8res, Trois-Rivi\u00e8res, Qu\u00e9bec, Canada","institution_ids":["https://openalex.org/I63341726"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.7441,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.76547063,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":"7","issue":null,"first_page":"3960","last_page":"3965"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11052","display_name":"Energy Load and Power Forecasting","score":0.9998000264167786,"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":0.9998000264167786,"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/T10121","display_name":"Building Energy and Comfort Optimization","score":0.9842000007629395,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.9840999841690063,"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/nonparametric-statistics","display_name":"Nonparametric statistics","score":0.7352287769317627},{"id":"https://openalex.org/keywords/autoregressive-model","display_name":"Autoregressive model","score":0.6888843178749084},{"id":"https://openalex.org/keywords/mean-absolute-percentage-error","display_name":"Mean absolute percentage error","score":0.6321521997451782},{"id":"https://openalex.org/keywords/kernel-density-estimation","display_name":"Kernel density estimation","score":0.6188861727714539},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.5829334855079651},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.5636014938354492},{"id":"https://openalex.org/keywords/demand-forecasting","display_name":"Demand forecasting","score":0.5607220530509949},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.5335754752159119},{"id":"https://openalex.org/keywords/electricity","display_name":"Electricity","score":0.5044463872909546},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4662401080131531},{"id":"https://openalex.org/keywords/demand-response","display_name":"Demand response","score":0.44323065876960754},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.44095325469970703},{"id":"https://openalex.org/keywords/probability-distribution","display_name":"Probability distribution","score":0.4404109716415405},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.43030235171318054},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.3536895513534546},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.23530706763267517},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.16570672392845154},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.14214548468589783},{"id":"https://openalex.org/keywords/operations-research","display_name":"Operations research","score":0.11663973331451416}],"concepts":[{"id":"https://openalex.org/C102366305","wikidata":"https://www.wikidata.org/wiki/Q1097688","display_name":"Nonparametric statistics","level":2,"score":0.7352287769317627},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.6888843178749084},{"id":"https://openalex.org/C150217764","wikidata":"https://www.wikidata.org/wiki/Q6803607","display_name":"Mean absolute percentage error","level":3,"score":0.6321521997451782},{"id":"https://openalex.org/C71134354","wikidata":"https://www.wikidata.org/wiki/Q458825","display_name":"Kernel density estimation","level":3,"score":0.6188861727714539},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.5829334855079651},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5636014938354492},{"id":"https://openalex.org/C193809577","wikidata":"https://www.wikidata.org/wiki/Q3409300","display_name":"Demand forecasting","level":2,"score":0.5607220530509949},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.5335754752159119},{"id":"https://openalex.org/C206658404","wikidata":"https://www.wikidata.org/wiki/Q12725","display_name":"Electricity","level":2,"score":0.5044463872909546},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4662401080131531},{"id":"https://openalex.org/C2779438525","wikidata":"https://www.wikidata.org/wiki/Q5255048","display_name":"Demand response","level":3,"score":0.44323065876960754},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.44095325469970703},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.4404109716415405},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.43030235171318054},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.3536895513534546},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.23530706763267517},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.16570672392845154},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.14214548468589783},{"id":"https://openalex.org/C42475967","wikidata":"https://www.wikidata.org/wiki/Q194292","display_name":"Operations research","level":1,"score":0.11663973331451416},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","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/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iecon.2016.7793935","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iecon.2016.7793935","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy","score":0.8399999737739563}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W1482574163","https://openalex.org/W1540375479","https://openalex.org/W1863377523","https://openalex.org/W1965419024","https://openalex.org/W1967509081","https://openalex.org/W1968968881","https://openalex.org/W1985825297","https://openalex.org/W1991681888","https://openalex.org/W2017377889","https://openalex.org/W2050354125","https://openalex.org/W2051635872","https://openalex.org/W2066944067","https://openalex.org/W2101806981","https://openalex.org/W2115451563","https://openalex.org/W2125919294","https://openalex.org/W2136886086","https://openalex.org/W2142104996","https://openalex.org/W2145732764","https://openalex.org/W2146520017","https://openalex.org/W2161597212","https://openalex.org/W2344581881","https://openalex.org/W2526261332","https://openalex.org/W2544706998","https://openalex.org/W3123412608","https://openalex.org/W4232142475","https://openalex.org/W6639034788","https://openalex.org/W6654943533"],"related_works":["https://openalex.org/W2555175041","https://openalex.org/W2032085210","https://openalex.org/W2389561653","https://openalex.org/W2052188148","https://openalex.org/W4239205706","https://openalex.org/W4239297229","https://openalex.org/W3003695187","https://openalex.org/W2621043410","https://openalex.org/W3166575991","https://openalex.org/W4210490979"],"abstract_inverted_index":{"This":[0,169],"paper":[1],"presents":[2],"a":[3,12,56,118],"new":[4],"methodology":[5,110],"for":[6,174],"house-hold":[7],"electricity":[8,28,43],"demand":[9,29,44,73,86,98],"forecast":[10,83],"using":[11,87,117],"hybrid":[13,156],"method":[14,58],"combining":[15],"nonparametric":[16,57,103],"model":[17,157],"and":[18,27,40,66,143,161],"time":[19],"series":[20],"analysis.":[21],"The":[22,106,127,150],"relationship":[23,65,99],"between":[24],"outdoor":[25],"temperature":[26,48],"is":[30,59,115,133,163],"studied":[31],"in":[32,45,112],"order":[33],"to":[34,38,47,61,82,120,180],"develop":[35],"an":[36,124],"approach":[37],"modeling":[39],"analyzing":[41],"the":[42,51,63,109,121,131,136,155,182],"response":[46],"changes.":[49],"First,":[50],"kernel":[52],"density":[53],"estimation":[54],"as":[55,100,102],"used":[60],"examine":[62],"mentioned":[64],"provide":[67],"probability":[68],"distribution":[69],"of":[70,95,108,123,130,138,177],"future":[71],"possible":[72],"values.":[74],"Second,":[75],"two":[76],"autoregressive":[77],"(AR)":[78],"models":[79,132],"are":[80],"applied":[81],"total":[84],"power":[85],"different":[88],"information":[89],"sources.":[90],"These":[91],"sources":[92],"comprise":[93],"results":[94,122,152,183],"forecasting":[96,128,151],"temperature/electricity":[97],"well":[101,160],"residual":[104],"data.":[105],"performance":[107],"represented":[111],"this":[113],"work":[114],"evaluated":[116],"comparison":[119],"ARMAX":[125,167],"model.":[126,168],"accuracy":[129],"compared":[134],"on":[135],"basis":[137],"mean":[139,144],"absolute":[140,145],"error":[141,147],"(MAE)":[142],"percentage":[146],"(MAPE)":[148],"metrics.":[149],"demonstrate":[153],"that":[154],"performs":[158],"remarkably":[159],"thus":[162],"more":[164],"favorable":[165],"than":[166],"study":[170],"employs":[171],"real":[172],"data":[173],"numerical":[175],"analysis":[176],"proposed":[178],"methods":[179],"increase":[181],"efficacy.":[184]},"counts_by_year":[{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":5},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}