{"id":"https://openalex.org/W2907164325","doi":"https://doi.org/10.1109/smartgridcomm.2018.8587494","title":"Residential Short-Term Load Forecasting Using Convolutional Neural Networks","display_name":"Residential Short-Term Load Forecasting Using Convolutional Neural Networks","publication_year":2018,"publication_date":"2018-10-01","ids":{"openalex":"https://openalex.org/W2907164325","doi":"https://doi.org/10.1109/smartgridcomm.2018.8587494","mag":"2907164325"},"language":"en","primary_location":{"id":"doi:10.1109/smartgridcomm.2018.8587494","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smartgridcomm.2018.8587494","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (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/A5004223032","display_name":"Marcus Vo\u00df","orcid":"https://orcid.org/0000-0002-7811-3561"},"institutions":[{"id":"https://openalex.org/I4577782","display_name":"Technische Universit\u00e4t Berlin","ror":"https://ror.org/03v4gjf40","country_code":"DE","type":"education","lineage":["https://openalex.org/I4577782"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Marcus Voss","raw_affiliation_strings":["Technische Universitat Berlin, Berlin, Germany"],"affiliations":[{"raw_affiliation_string":"Technische Universitat Berlin, Berlin, Germany","institution_ids":["https://openalex.org/I4577782"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5034892258","display_name":"Christian Bender-Saebelkampf","orcid":null},"institutions":[{"id":"https://openalex.org/I4577782","display_name":"Technische Universit\u00e4t Berlin","ror":"https://ror.org/03v4gjf40","country_code":"DE","type":"education","lineage":["https://openalex.org/I4577782"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Christian Bender-Saebelkampf","raw_affiliation_strings":["Technische Universit\u00e4t Berlin (DAI-Labor), Berlin, Germany"],"affiliations":[{"raw_affiliation_string":"Technische Universit\u00e4t Berlin (DAI-Labor), Berlin, Germany","institution_ids":["https://openalex.org/I4577782"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5089847337","display_name":"\u015eahin Albayrak","orcid":"https://orcid.org/0000-0001-5092-4584"},"institutions":[{"id":"https://openalex.org/I4577782","display_name":"Technische Universit\u00e4t Berlin","ror":"https://ror.org/03v4gjf40","country_code":"DE","type":"education","lineage":["https://openalex.org/I4577782"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Sahin Albayrak","raw_affiliation_strings":["Technische Universit\u00e4t Berlin (DAI-Labor), Berlin, Germany"],"affiliations":[{"raw_affiliation_string":"Technische Universit\u00e4t Berlin (DAI-Labor), Berlin, Germany","institution_ids":["https://openalex.org/I4577782"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5004223032"],"corresponding_institution_ids":["https://openalex.org/I4577782"],"apc_list":null,"apc_paid":null,"fwci":1.9313,"has_fulltext":false,"cited_by_count":47,"citation_normalized_percentile":{"value":0.87214773,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":97,"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":1.0,"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":1.0,"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/T11276","display_name":"Solar Radiation and Photovoltaics","score":0.9909999966621399,"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"}},{"id":"https://openalex.org/T10603","display_name":"Smart Grid Energy Management","score":0.9889000058174133,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.8282785415649414},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7504881620407104},{"id":"https://openalex.org/keywords/feature-engineering","display_name":"Feature engineering","score":0.6907540559768677},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6447668075561523},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.565740704536438},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.529183566570282},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.5203810930252075},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.5129077434539795},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5094373822212219},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4945935904979706},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.4421747028827667},{"id":"https://openalex.org/keywords/work","display_name":"Work (physics)","score":0.4223913252353668},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.3543117642402649},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.13647043704986572}],"concepts":[{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.8282785415649414},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7504881620407104},{"id":"https://openalex.org/C2778827112","wikidata":"https://www.wikidata.org/wiki/Q22245680","display_name":"Feature engineering","level":3,"score":0.6907540559768677},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6447668075561523},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.565740704536438},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.529183566570282},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.5203810930252075},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.5129077434539795},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5094373822212219},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4945935904979706},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.4421747028827667},{"id":"https://openalex.org/C18762648","wikidata":"https://www.wikidata.org/wiki/Q42213","display_name":"Work (physics)","level":2,"score":0.4223913252353668},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3543117642402649},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.13647043704986572},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"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/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/smartgridcomm.2018.8587494","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smartgridcomm.2018.8587494","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":40,"referenced_works":["https://openalex.org/W1000589962","https://openalex.org/W1538131130","https://openalex.org/W1876925204","https://openalex.org/W2008084603","https://openalex.org/W2030563576","https://openalex.org/W2094053746","https://openalex.org/W2095705004","https://openalex.org/W2101234009","https://openalex.org/W2126603600","https://openalex.org/W2145509823","https://openalex.org/W2296521892","https://openalex.org/W2473829957","https://openalex.org/W2490223215","https://openalex.org/W2519091744","https://openalex.org/W2543643230","https://openalex.org/W2562403923","https://openalex.org/W2563648892","https://openalex.org/W2592453717","https://openalex.org/W2593505840","https://openalex.org/W2597866042","https://openalex.org/W2603648311","https://openalex.org/W2742473260","https://openalex.org/W2754252319","https://openalex.org/W2766843231","https://openalex.org/W2771018930","https://openalex.org/W2774596248","https://openalex.org/W2776741657","https://openalex.org/W2781956327","https://openalex.org/W2949382160","https://openalex.org/W2963840672","https://openalex.org/W3104996215","https://openalex.org/W6632100814","https://openalex.org/W6674330103","https://openalex.org/W6675354045","https://openalex.org/W6696085341","https://openalex.org/W6720703399","https://openalex.org/W6735826779","https://openalex.org/W6743985089","https://openalex.org/W6746162764","https://openalex.org/W6746737992"],"related_works":["https://openalex.org/W2378211422","https://openalex.org/W4321353415","https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3133861977","https://openalex.org/W2951211570","https://openalex.org/W3103566983","https://openalex.org/W4360615883","https://openalex.org/W2965782936","https://openalex.org/W3034267371"],"abstract_inverted_index":{"Low":[0],"aggregations":[1,174],"of":[2,109,134,175],"electric":[3,112],"load":[4,166,201],"profiles":[5],"are":[6,12,57],"more":[7],"fluctuating,":[8],"relative":[9],"forecast":[10,22,55],"errors":[11],"comparatively":[13],"high,":[14],"and":[15,24,47,95,121,148,165],"it":[16,196],"has":[17,84],"been":[18,85],"shown":[19],"that":[20,116,141],"different":[21],"models":[23,127],"feature":[25,45,78],"configurations":[26],"may":[27],"be":[28,168],"best":[29],"suitable":[30,105,198],"for":[31,43,76,92,106,128,152,199],"specific":[32],"households":[33,177],"or":[34],"buildings.":[35],"However,":[36],"at":[37,131,192],"low":[38],"aggregations,":[39],"the":[40,132,160,182],"monetary":[41],"incentive":[42],"manual":[44,77],"engineering":[46],"model":[48,136],"selection":[49],"is":[50,104],"low,":[51],"as":[52,155,159,170],"benefits":[53],"from":[54],"improvements":[56],"small.":[58],"Convolutional":[59],"Neural":[60,190],"Networks":[61,191],"(CNN)":[62],"have":[63],"proven":[64],"to":[65,87,187],"achieve":[66],"high":[67],"accuracy":[68],"in":[69],"an":[70],"end-to-end":[71],"fashion":[72],"with":[73],"minimal":[74],"effort":[75],"selection.":[79],"WaveNet,":[80],"a":[81,156],"CNN-based":[82],"approach,":[83],"developed":[86],"handle":[88],"noisy":[89],"time-series":[90],"data":[91],"speech":[93],"recognition":[94],"synthesis.":[96],"In":[97],"this":[98],"work":[99],"we":[100],"explore":[101],"if":[102],"WaveNet":[103,117,178],"short-term":[107],"forecasts":[108],"lowly":[110],"aggregated":[111,200],"loads.":[113],"We":[114],"find":[115],"performs":[118],"similarly":[119],"to,":[120],"slightly":[122],"better":[123],"than,":[124],"typical":[125],"benchmark":[126],"individual":[129,153],"households,":[130,154,194],"cost":[133],"higher":[135],"complexity.":[137],"Preliminary":[138],"experiments":[139],"show":[140],"transfer":[142],"learning":[143],"can":[144,167],"further":[145],"improve":[146],"results":[147],"decrease":[149],"training":[150],"times":[151],"pattern":[157],"such":[158],"correlation":[161],"between":[162],"outside":[163],"temperature":[164],"learned":[169],"general":[171],"features.":[172],"For":[173],"10-200":[176],"improves":[179],"most":[180],"over":[181],"benchmarks,":[183],"e.g.,":[184],"13%":[185],"compared":[186],"vanilla":[188],"Artificial":[189],"200":[193],"making":[195],"possibly":[197],"forecasting.":[202]},"counts_by_year":[{"year":2025,"cited_by_count":7},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":11},{"year":2022,"cited_by_count":7},{"year":2021,"cited_by_count":5},{"year":2020,"cited_by_count":5},{"year":2019,"cited_by_count":5}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
