{"id":"https://openalex.org/W2771601186","doi":"https://doi.org/10.1109/iecon.2017.8217295","title":"A hybrid method for short-term electricity consumption prediction","display_name":"A hybrid method for short-term electricity consumption prediction","publication_year":2017,"publication_date":"2017-10-01","ids":{"openalex":"https://openalex.org/W2771601186","doi":"https://doi.org/10.1109/iecon.2017.8217295","mag":"2771601186"},"language":"en","primary_location":{"id":"doi:10.1109/iecon.2017.8217295","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iecon.2017.8217295","pdf_url":null,"source":{"id":"https://openalex.org/S4363608531","display_name":"IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IECON 2017 - 43rd 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/A5100620844","display_name":"Xiao-Zhi Gao","orcid":"https://orcid.org/0000-0001-6100-971X"},"institutions":[{"id":"https://openalex.org/I63548447","display_name":"Lappeenranta-Lahti University of Technology","ror":"https://ror.org/0208vgz68","country_code":"FI","type":"education","lineage":["https://openalex.org/I63548447"]}],"countries":["FI"],"is_corresponding":true,"raw_author_name":"X. Z. Gao","raw_affiliation_strings":["Machine Vision and Pattern Recognition Laboratory, Lappeenranta University of Technology, Finland"],"affiliations":[{"raw_affiliation_string":"Machine Vision and Pattern Recognition Laboratory, Lappeenranta University of Technology, Finland","institution_ids":["https://openalex.org/I63548447"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067988452","display_name":"Arto Kaarna","orcid":"https://orcid.org/0000-0002-9320-6504"},"institutions":[{"id":"https://openalex.org/I63548447","display_name":"Lappeenranta-Lahti University of Technology","ror":"https://ror.org/0208vgz68","country_code":"FI","type":"education","lineage":["https://openalex.org/I63548447"]}],"countries":["FI"],"is_corresponding":false,"raw_author_name":"A. Kaarna","raw_affiliation_strings":["Machine Vision and Pattern Recognition Laboratory, Lappeenranta University of Technology, Finland"],"affiliations":[{"raw_affiliation_string":"Machine Vision and Pattern Recognition Laboratory, Lappeenranta University of Technology, Finland","institution_ids":["https://openalex.org/I63548447"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066670639","display_name":"Lasse Lensu","orcid":"https://orcid.org/0000-0002-7691-121X"},"institutions":[{"id":"https://openalex.org/I63548447","display_name":"Lappeenranta-Lahti University of Technology","ror":"https://ror.org/0208vgz68","country_code":"FI","type":"education","lineage":["https://openalex.org/I63548447"]}],"countries":["FI"],"is_corresponding":false,"raw_author_name":"L. Lensu","raw_affiliation_strings":["Machine Vision and Pattern Recognition Laboratory, Lappeenranta University of Technology, Finland"],"affiliations":[{"raw_affiliation_string":"Machine Vision and Pattern Recognition Laboratory, Lappeenranta University of Technology, Finland","institution_ids":["https://openalex.org/I63548447"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5049376408","display_name":"Samuli Honkapuro","orcid":"https://orcid.org/0000-0001-8761-474X"},"institutions":[{"id":"https://openalex.org/I63548447","display_name":"Lappeenranta-Lahti University of Technology","ror":"https://ror.org/0208vgz68","country_code":"FI","type":"education","lineage":["https://openalex.org/I63548447"]}],"countries":["FI"],"is_corresponding":false,"raw_author_name":"S. Honkapuro","raw_affiliation_strings":["School of Energy Systems, Lappeenranta University of Technology, Finland"],"affiliations":[{"raw_affiliation_string":"School of Energy Systems, Lappeenranta University of Technology, Finland","institution_ids":["https://openalex.org/I63548447"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5100620844"],"corresponding_institution_ids":["https://openalex.org/I63548447"],"apc_list":null,"apc_paid":null,"fwci":0.5165,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.5527927,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"7393","last_page":"7398"},"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/T12368","display_name":"Grey System Theory Applications","score":0.9930999875068665,"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/T10320","display_name":"Neural Networks and Applications","score":0.9912999868392944,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.7434628009796143},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6717437505722046},{"id":"https://openalex.org/keywords/electricity","display_name":"Electricity","score":0.6314429044723511},{"id":"https://openalex.org/keywords/mean-squared-prediction-error","display_name":"Mean squared prediction error","score":0.5707398056983948},{"id":"https://openalex.org/keywords/linear-regression","display_name":"Linear regression","score":0.5702824592590332},{"id":"https://openalex.org/keywords/power-consumption","display_name":"Power consumption","score":0.5698713660240173},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.5656874775886536},{"id":"https://openalex.org/keywords/consumption","display_name":"Consumption (sociology)","score":0.5421536564826965},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.5410001277923584},{"id":"https://openalex.org/keywords/predictive-modelling","display_name":"Predictive modelling","score":0.5107381343841553},{"id":"https://openalex.org/keywords/predictive-power","display_name":"Predictive power","score":0.5090150833129883},{"id":"https://openalex.org/keywords/long-term-prediction","display_name":"Long-term prediction","score":0.4920441806316376},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.46656835079193115},{"id":"https://openalex.org/keywords/power","display_name":"Power (physics)","score":0.44276002049446106},{"id":"https://openalex.org/keywords/linear-prediction","display_name":"Linear prediction","score":0.4258017838001251},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.40231919288635254},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.36591702699661255},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.20999905467033386},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.19638755917549133},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.16950547695159912},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.12914583086967468},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.06991937756538391}],"concepts":[{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.7434628009796143},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6717437505722046},{"id":"https://openalex.org/C206658404","wikidata":"https://www.wikidata.org/wiki/Q12725","display_name":"Electricity","level":2,"score":0.6314429044723511},{"id":"https://openalex.org/C167085575","wikidata":"https://www.wikidata.org/wiki/Q6803654","display_name":"Mean squared prediction error","level":2,"score":0.5707398056983948},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.5702824592590332},{"id":"https://openalex.org/C2984118289","wikidata":"https://www.wikidata.org/wiki/Q29954","display_name":"Power consumption","level":3,"score":0.5698713660240173},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.5656874775886536},{"id":"https://openalex.org/C30772137","wikidata":"https://www.wikidata.org/wiki/Q5164762","display_name":"Consumption (sociology)","level":2,"score":0.5421536564826965},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.5410001277923584},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.5107381343841553},{"id":"https://openalex.org/C2778136018","wikidata":"https://www.wikidata.org/wiki/Q10350689","display_name":"Predictive power","level":2,"score":0.5090150833129883},{"id":"https://openalex.org/C2776537626","wikidata":"https://www.wikidata.org/wiki/Q4047883","display_name":"Long-term prediction","level":2,"score":0.4920441806316376},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.46656835079193115},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.44276002049446106},{"id":"https://openalex.org/C131109320","wikidata":"https://www.wikidata.org/wiki/Q581012","display_name":"Linear prediction","level":2,"score":0.4258017838001251},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.40231919288635254},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.36591702699661255},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.20999905467033386},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.19638755917549133},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.16950547695159912},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.12914583086967468},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.06991937756538391},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"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/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C36289849","wikidata":"https://www.wikidata.org/wiki/Q34749","display_name":"Social science","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},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"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/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iecon.2017.8217295","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iecon.2017.8217295","pdf_url":null,"source":{"id":"https://openalex.org/S4363608531","display_name":"IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society","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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W1971594877","https://openalex.org/W1974712709","https://openalex.org/W1982832202","https://openalex.org/W2034226878","https://openalex.org/W2051795269","https://openalex.org/W2066269320","https://openalex.org/W2104686690","https://openalex.org/W2116048577","https://openalex.org/W2122671960","https://openalex.org/W2165022825","https://openalex.org/W2171697319","https://openalex.org/W2264139484","https://openalex.org/W2328146686"],"related_works":["https://openalex.org/W3189884647","https://openalex.org/W2884834684","https://openalex.org/W2917165927","https://openalex.org/W4385950391","https://openalex.org/W409878841","https://openalex.org/W3123614577","https://openalex.org/W3023917431","https://openalex.org/W4311802502","https://openalex.org/W1998320186","https://openalex.org/W3192139338"],"abstract_inverted_index":{"Electricity":[0],"consumption":[1],"prediction":[2,21,62,76],"is":[3,16,56],"an":[4,43],"important":[5],"but":[6],"demanding":[7],"issue":[8],"in":[9,32,81],"the":[10,19,33,61,70,93],"study":[11,85],"of":[12,35],"power":[13,36,66],"systems.":[14],"It":[15],"difficult":[17],"for":[18,60],"conventional":[20],"methods,":[22],"such":[23],"as":[24],"linear":[25],"models,":[26],"to":[27,58,65,86],"utilize":[28],"relevant":[29],"domain":[30],"knowledge":[31],"forecasting":[34],"peaks.":[37],"In":[38],"this":[39],"paper,":[40],"we":[41],"propose":[42],"approach":[44],"merging":[45],"a":[46,50,82],"regression":[47,95],"predictor":[48],"and":[49],"peak":[51],"compensator":[52],"together.":[53],"The":[54,72],"latter":[55],"designed":[57],"compensate":[59],"errors":[63],"related":[64],"peaks":[67],"caused":[68],"by":[69],"former.":[71],"proposed":[73],"hybrid":[74],"short-term":[75],"scheme":[77],"has":[78],"been":[79],"demonstrated":[80],"real-world":[83],"case":[84],"efficiently":[87],"yield":[88],"performances":[89],"moderately":[90],"better":[91],"than":[92],"standalone":[94],"predictors.":[96]},"counts_by_year":[{"year":2020,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
