{"id":"https://openalex.org/W3170491487","doi":"https://doi.org/10.1109/tetci.2021.3086226","title":"Double Fourier Integral Analysis Based Convolutional Neural Network Regression for High-Frequency Energy Disaggregation","display_name":"Double Fourier Integral Analysis Based Convolutional Neural Network Regression for High-Frequency Energy Disaggregation","publication_year":2021,"publication_date":"2021-06-16","ids":{"openalex":"https://openalex.org/W3170491487","doi":"https://doi.org/10.1109/tetci.2021.3086226","mag":"3170491487"},"language":"en","primary_location":{"id":"doi:10.1109/tetci.2021.3086226","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tetci.2021.3086226","pdf_url":null,"source":{"id":"https://openalex.org/S4210210251","display_name":"IEEE Transactions on Emerging Topics in Computational Intelligence","issn_l":"2471-285X","issn":["2471-285X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Emerging Topics in Computational Intelligence","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://uhra.herts.ac.uk/id/eprint/9087/7/Double_Fourier_Integral_Analysis_NILM_J_1_.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5084960932","display_name":"Pascal A. Schirmer","orcid":"https://orcid.org/0000-0001-5434-4739"},"institutions":[{"id":"https://openalex.org/I141584323","display_name":"University of Hertfordshire","ror":"https://ror.org/0267vjk41","country_code":"GB","type":"education","lineage":["https://openalex.org/I141584323"]}],"countries":["GB"],"is_corresponding":true,"raw_author_name":"Pascal A. Schirmer","raw_affiliation_strings":["School of Physics, Engineering and Computer Science, Intelligent Systems Group CIS, University of Hertfordshire, Hatfield, U.K"],"affiliations":[{"raw_affiliation_string":"School of Physics, Engineering and Computer Science, Intelligent Systems Group CIS, University of Hertfordshire, Hatfield, U.K","institution_ids":["https://openalex.org/I141584323"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5016006232","display_name":"Iosif Mporas","orcid":"https://orcid.org/0000-0001-6984-0268"},"institutions":[{"id":"https://openalex.org/I141584323","display_name":"University of Hertfordshire","ror":"https://ror.org/0267vjk41","country_code":"GB","type":"education","lineage":["https://openalex.org/I141584323"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Iosif Mporas","raw_affiliation_strings":["School of Physics, Engineering and Computer Science, Intelligent Systems Group CIS, University of Hertfordshire, Hatfield, U.K"],"affiliations":[{"raw_affiliation_string":"School of Physics, Engineering and Computer Science, Intelligent Systems Group CIS, University of Hertfordshire, Hatfield, U.K","institution_ids":["https://openalex.org/I141584323"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5084960932"],"corresponding_institution_ids":["https://openalex.org/I141584323"],"apc_list":null,"apc_paid":null,"fwci":1.6191,"has_fulltext":true,"cited_by_count":26,"citation_normalized_percentile":{"value":0.83313427,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":"6","issue":"3","first_page":"439","last_page":"449"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10603","display_name":"Smart Grid Energy Management","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/T10603","display_name":"Smart Grid Energy Management","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/T10121","display_name":"Building Energy and Comfort Optimization","score":0.9957000017166138,"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/T11052","display_name":"Energy Load and Power Forecasting","score":0.9926000237464905,"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/spectrogram","display_name":"Spectrogram","score":0.8533953428268433},{"id":"https://openalex.org/keywords/fourier-transform","display_name":"Fourier transform","score":0.6820012927055359},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5938126444816589},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.590724527835846},{"id":"https://openalex.org/keywords/energy","display_name":"Energy (signal processing)","score":0.5835424661636353},{"id":"https://openalex.org/keywords/short-time-fourier-transform","display_name":"Short-time Fourier transform","score":0.5692424178123474},{"id":"https://openalex.org/keywords/voltage","display_name":"Voltage","score":0.5558950901031494},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.5232438445091248},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.5217167735099792},{"id":"https://openalex.org/keywords/fourier-analysis","display_name":"Fourier analysis","score":0.46786168217658997},{"id":"https://openalex.org/keywords/power","display_name":"Power (physics)","score":0.4546622633934021},{"id":"https://openalex.org/keywords/smart-meter","display_name":"Smart meter","score":0.43449151515960693},{"id":"https://openalex.org/keywords/energy-consumption","display_name":"Energy consumption","score":0.41725271940231323},{"id":"https://openalex.org/keywords/linear-regression","display_name":"Linear regression","score":0.4106849730014801},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3792531192302704},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3435570001602173},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.2580947279930115},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2252722978591919},{"id":"https://openalex.org/keywords/electricity","display_name":"Electricity","score":0.1902763545513153},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.1835317313671112},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.16411903500556946},{"id":"https://openalex.org/keywords/electrical-engineering","display_name":"Electrical engineering","score":0.14219757914543152},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.11227533221244812}],"concepts":[{"id":"https://openalex.org/C45273575","wikidata":"https://www.wikidata.org/wiki/Q578970","display_name":"Spectrogram","level":2,"score":0.8533953428268433},{"id":"https://openalex.org/C102519508","wikidata":"https://www.wikidata.org/wiki/Q6520159","display_name":"Fourier transform","level":2,"score":0.6820012927055359},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5938126444816589},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.590724527835846},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.5835424661636353},{"id":"https://openalex.org/C166386157","wikidata":"https://www.wikidata.org/wiki/Q1477735","display_name":"Short-time Fourier transform","level":4,"score":0.5692424178123474},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.5558950901031494},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.5232438445091248},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.5217167735099792},{"id":"https://openalex.org/C203024314","wikidata":"https://www.wikidata.org/wiki/Q1365258","display_name":"Fourier analysis","level":3,"score":0.46786168217658997},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.4546622633934021},{"id":"https://openalex.org/C2779510800","wikidata":"https://www.wikidata.org/wiki/Q1630602","display_name":"Smart meter","level":3,"score":0.43449151515960693},{"id":"https://openalex.org/C2780165032","wikidata":"https://www.wikidata.org/wiki/Q16869822","display_name":"Energy consumption","level":2,"score":0.41725271940231323},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.4106849730014801},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3792531192302704},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3435570001602173},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2580947279930115},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2252722978591919},{"id":"https://openalex.org/C206658404","wikidata":"https://www.wikidata.org/wiki/Q12725","display_name":"Electricity","level":2,"score":0.1902763545513153},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.1835317313671112},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.16411903500556946},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.14219757914543152},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.11227533221244812},{"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/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tetci.2021.3086226","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tetci.2021.3086226","pdf_url":null,"source":{"id":"https://openalex.org/S4210210251","display_name":"IEEE Transactions on Emerging Topics in Computational Intelligence","issn_l":"2471-285X","issn":["2471-285X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Emerging Topics in Computational Intelligence","raw_type":"journal-article"},{"id":"pmh:oai:uhra.herts.ac.uk:2299/24592","is_oa":true,"landing_page_url":"http://hdl.handle.net/2299/24592","pdf_url":"https://uhra.herts.ac.uk/id/eprint/9087/7/Double_Fourier_Integral_Analysis_NILM_J_1_.pdf","source":{"id":"https://openalex.org/S4306400241","display_name":"University of Hertfordshire Research Archive (University of Hertfordshire)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I141584323","host_organization_name":"University of Hertfordshire","host_organization_lineage":["https://openalex.org/I141584323"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":null}],"best_oa_location":{"id":"pmh:oai:uhra.herts.ac.uk:2299/24592","is_oa":true,"landing_page_url":"http://hdl.handle.net/2299/24592","pdf_url":"https://uhra.herts.ac.uk/id/eprint/9087/7/Double_Fourier_Integral_Analysis_NILM_J_1_.pdf","source":{"id":"https://openalex.org/S4306400241","display_name":"University of Hertfordshire Research Archive (University of Hertfordshire)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I141584323","host_organization_name":"University of Hertfordshire","host_organization_lineage":["https://openalex.org/I141584323"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":null},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/7","score":0.9100000262260437,"display_name":"Affordable and clean energy"}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3170491487.pdf","grobid_xml":"https://content.openalex.org/works/W3170491487.grobid-xml"},"referenced_works_count":68,"referenced_works":["https://openalex.org/W1479651931","https://openalex.org/W1971568424","https://openalex.org/W1987077486","https://openalex.org/W1996944908","https://openalex.org/W2006478669","https://openalex.org/W2043934529","https://openalex.org/W2055106261","https://openalex.org/W2060073953","https://openalex.org/W2078921071","https://openalex.org/W2101743194","https://openalex.org/W2104047613","https://openalex.org/W2108281209","https://openalex.org/W2122681436","https://openalex.org/W2123070597","https://openalex.org/W2123910460","https://openalex.org/W2141213980","https://openalex.org/W2160897148","https://openalex.org/W2163121678","https://openalex.org/W2183497669","https://openalex.org/W2209987497","https://openalex.org/W2255051834","https://openalex.org/W2283966878","https://openalex.org/W2292311507","https://openalex.org/W2292806482","https://openalex.org/W2343090960","https://openalex.org/W2344234701","https://openalex.org/W2515519620","https://openalex.org/W2548908563","https://openalex.org/W2581463316","https://openalex.org/W2586240443","https://openalex.org/W2697608749","https://openalex.org/W2747580724","https://openalex.org/W2749218967","https://openalex.org/W2750941888","https://openalex.org/W2786091076","https://openalex.org/W2795358293","https://openalex.org/W2806858326","https://openalex.org/W2890533905","https://openalex.org/W2900536921","https://openalex.org/W2904341746","https://openalex.org/W2904841534","https://openalex.org/W2940200204","https://openalex.org/W2942178783","https://openalex.org/W2946808087","https://openalex.org/W2949682416","https://openalex.org/W2952067596","https://openalex.org/W2952167052","https://openalex.org/W2953171230","https://openalex.org/W2954581781","https://openalex.org/W2971067036","https://openalex.org/W2972586903","https://openalex.org/W2994244939","https://openalex.org/W2995320218","https://openalex.org/W2998326830","https://openalex.org/W3009638651","https://openalex.org/W3011220517","https://openalex.org/W3015396216","https://openalex.org/W3016117162","https://openalex.org/W3031361451","https://openalex.org/W3036436050","https://openalex.org/W3099873379","https://openalex.org/W3102977103","https://openalex.org/W3141456309","https://openalex.org/W6627345084","https://openalex.org/W6704800599","https://openalex.org/W6731843568","https://openalex.org/W6748514494","https://openalex.org/W6936597892"],"related_works":["https://openalex.org/W2120540196","https://openalex.org/W3095343173","https://openalex.org/W2381036744","https://openalex.org/W2288135719","https://openalex.org/W2323749021","https://openalex.org/W2533590149","https://openalex.org/W2901989338","https://openalex.org/W82005754","https://openalex.org/W2334448276","https://openalex.org/W3210733254"],"abstract_inverted_index":{"Non-Intrusive":[0,36],"Load":[1,37],"Monitoring":[2,38],"aims":[3],"to":[4,42,49,79,111],"extract":[5],"the":[6,16,35,55,89,95,104,112],"energy":[7,114],"consumption":[8],"of":[9,15,88],"individual":[10],"electrical":[11],"appliances":[12],"through":[13],"disaggregation":[14,115],"total":[17],"power":[18],"load":[19],"measured":[20],"by":[21,70,107],"a":[22,80],"single":[23],"smart-meter.":[24],"In":[25],"this":[26],"article":[27],"we":[28],"introduce":[29],"Double":[30,71],"Fourier":[31,72],"Integral":[32,73],"Analysis":[33,74],"in":[34,40,94],"task":[39],"order":[41],"provide":[43],"more":[44],"distinct":[45],"feature":[46],"descriptions":[47],"compared":[48,110],"current":[50,58,118],"or":[51],"voltage":[52,60,120],"spectrograms.":[53,121],"Specifically,":[54],"high-frequency":[56],"aggregated":[57],"and":[59,75,119],"signals":[61],"are":[62],"transformed":[63],"into":[64],"two-dimensional":[65],"unit":[66],"cells":[67],"as":[68,77],"calculated":[69],"used":[76],"input":[78],"Convolutional":[81],"Neural":[82],"Network":[83],"for":[84],"regression.":[85],"The":[86,100],"performance":[87],"proposed":[90,101],"methodology":[91],"was":[92],"evaluated":[93],"publicly":[96],"available":[97],"U.K.-DALE":[98],"dataset.":[99],"approach":[102],"improves":[103],"estimation":[105],"accuracy":[106],"7.2%":[108],"when":[109],"baseline":[113],"setup":[116],"using":[117]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":9},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":5}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}
