{"id":"https://openalex.org/W2798744368","doi":"https://doi.org/10.1109/smartgridcomm.2017.8340706","title":"Recursive power demand prediction based on multi-level clustering of power demand data","display_name":"Recursive power demand prediction based on multi-level clustering of power demand data","publication_year":2017,"publication_date":"2017-10-01","ids":{"openalex":"https://openalex.org/W2798744368","doi":"https://doi.org/10.1109/smartgridcomm.2017.8340706","mag":"2798744368"},"language":"en","primary_location":{"id":"doi:10.1109/smartgridcomm.2017.8340706","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smartgridcomm.2017.8340706","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Smart Grid Communications (SmartGridComm)","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/A5085267972","display_name":"Sai Akhil R. Konakalla","orcid":"https://orcid.org/0000-0002-6809-9938"},"institutions":[{"id":"https://openalex.org/I36258959","display_name":"University of California, San Diego","ror":"https://ror.org/0168r3w48","country_code":"US","type":"education","lineage":["https://openalex.org/I36258959"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Sai Akhil R. Konakalla","raw_affiliation_strings":["University of California San Diego, La Jolla, CA, US"],"affiliations":[{"raw_affiliation_string":"University of California San Diego, La Jolla, CA, US","institution_ids":["https://openalex.org/I36258959"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5028003129","display_name":"Raymond de Callafon","orcid":null},"institutions":[{"id":"https://openalex.org/I36258959","display_name":"University of California, San Diego","ror":"https://ror.org/0168r3w48","country_code":"US","type":"education","lineage":["https://openalex.org/I36258959"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Raymond de Callafon","raw_affiliation_strings":["Department of Mechanical and Aerospace Engineering, University of California San Diego, San Diego, USA"],"affiliations":[{"raw_affiliation_string":"Department of Mechanical and Aerospace Engineering, University of California San Diego, San Diego, USA","institution_ids":["https://openalex.org/I36258959"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5085267972"],"corresponding_institution_ids":["https://openalex.org/I36258959"],"apc_list":null,"apc_paid":null,"fwci":0.1433,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.55044342,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"247","last_page":"252"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11052","display_name":"Energy Load and Power Forecasting","score":0.9995999932289124,"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":0.9995999932289124,"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/T10603","display_name":"Smart Grid Energy Management","score":0.996399998664856,"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":0.9939000010490417,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.8942615985870361},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6525312066078186},{"id":"https://openalex.org/keywords/power-demand","display_name":"Power demand","score":0.6253350973129272},{"id":"https://openalex.org/keywords/scheduling","display_name":"Scheduling (production processes)","score":0.5048530697822571},{"id":"https://openalex.org/keywords/principal-component-analysis","display_name":"Principal component analysis","score":0.5008187294006348},{"id":"https://openalex.org/keywords/power-consumption","display_name":"Power consumption","score":0.4997422695159912},{"id":"https://openalex.org/keywords/demand-forecasting","display_name":"Demand forecasting","score":0.44225162267684937},{"id":"https://openalex.org/keywords/cure-data-clustering-algorithm","display_name":"CURE data clustering algorithm","score":0.4312543570995331},{"id":"https://openalex.org/keywords/power","display_name":"Power (physics)","score":0.42487382888793945},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.38131046295166016},{"id":"https://openalex.org/keywords/correlation-clustering","display_name":"Correlation clustering","score":0.36010462045669556},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.35601407289505005},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.20216310024261475},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.1882764995098114},{"id":"https://openalex.org/keywords/operations-research","display_name":"Operations research","score":0.13058289885520935}],"concepts":[{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.8942615985870361},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6525312066078186},{"id":"https://openalex.org/C2983317576","wikidata":"https://www.wikidata.org/wiki/Q1853339","display_name":"Power demand","level":4,"score":0.6253350973129272},{"id":"https://openalex.org/C206729178","wikidata":"https://www.wikidata.org/wiki/Q2271896","display_name":"Scheduling (production processes)","level":2,"score":0.5048530697822571},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.5008187294006348},{"id":"https://openalex.org/C2984118289","wikidata":"https://www.wikidata.org/wiki/Q29954","display_name":"Power consumption","level":3,"score":0.4997422695159912},{"id":"https://openalex.org/C193809577","wikidata":"https://www.wikidata.org/wiki/Q3409300","display_name":"Demand forecasting","level":2,"score":0.44225162267684937},{"id":"https://openalex.org/C33704608","wikidata":"https://www.wikidata.org/wiki/Q5014717","display_name":"CURE data clustering algorithm","level":4,"score":0.4312543570995331},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.42487382888793945},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.38131046295166016},{"id":"https://openalex.org/C94641424","wikidata":"https://www.wikidata.org/wiki/Q5172845","display_name":"Correlation clustering","level":3,"score":0.36010462045669556},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.35601407289505005},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.20216310024261475},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.1882764995098114},{"id":"https://openalex.org/C42475967","wikidata":"https://www.wikidata.org/wiki/Q194292","display_name":"Operations research","level":1,"score":0.13058289885520935},{"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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/smartgridcomm.2017.8340706","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smartgridcomm.2017.8340706","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Smart Grid Communications (SmartGridComm)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/7","score":0.699999988079071,"display_name":"Affordable and clean energy"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W153870454","https://openalex.org/W1535136082","https://openalex.org/W1541843935","https://openalex.org/W1982516036","https://openalex.org/W1995027390","https://openalex.org/W1995351367","https://openalex.org/W2013032827","https://openalex.org/W2036495557","https://openalex.org/W2105934661","https://openalex.org/W2135651192","https://openalex.org/W2154216109","https://openalex.org/W2241498016","https://openalex.org/W2251214836","https://openalex.org/W2264139484","https://openalex.org/W2294644361","https://openalex.org/W2335993985","https://openalex.org/W2557700421","https://openalex.org/W2583661442","https://openalex.org/W3102254722","https://openalex.org/W4233091538","https://openalex.org/W4236323066","https://openalex.org/W4250657332","https://openalex.org/W6690260054"],"related_works":["https://openalex.org/W2559422900","https://openalex.org/W2171610853","https://openalex.org/W3144143113","https://openalex.org/W3120229345","https://openalex.org/W3022637481","https://openalex.org/W2491448268","https://openalex.org/W2394117789","https://openalex.org/W2160785859","https://openalex.org/W2390610678","https://openalex.org/W2394193399"],"abstract_inverted_index":{"In":[0],"this":[1],"paper":[2],"an":[3],"algorithm":[4,22,113],"is":[5,50,91,114],"presented":[6],"that":[7,76],"provides":[8],"the":[9,94,104,112],"opportunity":[10],"to":[11,35,52,73],"recursively":[12,36],"update":[13,37],"day-ahead":[14,38,140],"electric":[15],"power":[16,32,39,43,88,118,129],"prediction":[17,90,131],"for":[18,31],"demand":[19,40,56,89,130],"scheduling.":[20],"The":[21,109],"uses":[23],"principal":[24],"component":[25],"analysis":[26],"(PCA)":[27],"based":[28,106],"multi-level":[29,107],"clustering":[30,48,69,137],"consumption":[33,44],"modeling":[34],"predictions.":[41],"For":[42],"modeling,":[45],"a":[46,63,66,86,99],"1-level":[47,134],"technique":[49,70],"used":[51],"first":[53],"distinguish":[54],"between":[55],"on":[57,93],"working":[58,126],"and":[59,81,138],"non-working":[60],"days.":[61],"Next,":[62],"refinement":[64],"using":[65,133],"2-level":[67,136],"sub-space":[68],"allows":[71],"correlation":[72],"exogenous":[74],"variables":[75],"include":[77],"weather,":[78],"extra-curricular":[79],"activities":[80],"planned":[82],"load":[83],"conditions.":[84],"Finally,":[85],"recursive":[87,139],"formulated":[92],"directional":[95],"basis":[96],"obtained":[97],"from":[98],"singular":[100],"value":[101],"decomposition":[102],"of":[103,111,121],"PCA":[105],"clustering.":[108],"performance":[110],"confirmed":[115],"by":[116],"comparing":[117],"usage":[119],"data":[120],"randomly":[122],"chosen":[123],"weather":[124],"affected":[125],"days":[127],"with":[128],"results":[132],"clustering,":[135],"updates.":[141]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2019,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
