{"id":"https://openalex.org/W4383220037","doi":"https://doi.org/10.1109/access.2023.3292516","title":"Knowledge Extraction From PV Power Generation With Deep Learning Autoencoder and Clustering-Based Algorithms","display_name":"Knowledge Extraction From PV Power Generation With Deep Learning Autoencoder and Clustering-Based Algorithms","publication_year":2023,"publication_date":"2023-01-01","ids":{"openalex":"https://openalex.org/W4383220037","doi":"https://doi.org/10.1109/access.2023.3292516"},"language":"en","primary_location":{"id":"doi:10.1109/access.2023.3292516","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2023.3292516","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10173524.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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 Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10173524.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5003174186","display_name":"Seyed Mahdi Miraftabzadeh","orcid":"https://orcid.org/0000-0002-4746-2208"},"institutions":[{"id":"https://openalex.org/I93860229","display_name":"Politecnico di Milano","ror":"https://ror.org/01nffqt88","country_code":"IT","type":"education","lineage":["https://openalex.org/I93860229"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Seyed Mahdi Miraftabzadeh","raw_affiliation_strings":["Department of Energy, Politecnico di Milano, Milan, Italy"],"raw_orcid":"https://orcid.org/0000-0002-4746-2208","affiliations":[{"raw_affiliation_string":"Department of Energy, Politecnico di Milano, Milan, Italy","institution_ids":["https://openalex.org/I93860229"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074047579","display_name":"Michela Longo","orcid":"https://orcid.org/0000-0002-3780-4980"},"institutions":[{"id":"https://openalex.org/I93860229","display_name":"Politecnico di Milano","ror":"https://ror.org/01nffqt88","country_code":"IT","type":"education","lineage":["https://openalex.org/I93860229"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Michela Longo","raw_affiliation_strings":["Department of Energy, Politecnico di Milano, Milan, Italy"],"raw_orcid":"https://orcid.org/0000-0002-3780-4980","affiliations":[{"raw_affiliation_string":"Department of Energy, Politecnico di Milano, Milan, Italy","institution_ids":["https://openalex.org/I93860229"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5060138668","display_name":"Morris Brenna","orcid":"https://orcid.org/0000-0002-6929-0523"},"institutions":[{"id":"https://openalex.org/I93860229","display_name":"Politecnico di Milano","ror":"https://ror.org/01nffqt88","country_code":"IT","type":"education","lineage":["https://openalex.org/I93860229"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Morris Brenna","raw_affiliation_strings":["Department of Energy, Politecnico di Milano, Milan, Italy"],"raw_orcid":"https://orcid.org/0000-0002-6929-0523","affiliations":[{"raw_affiliation_string":"Department of Energy, Politecnico di Milano, Milan, Italy","institution_ids":["https://openalex.org/I93860229"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I93860229"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":2.91,"has_fulltext":true,"cited_by_count":24,"citation_normalized_percentile":{"value":0.91396107,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":95,"max":99},"biblio":{"volume":"11","issue":null,"first_page":"69227","last_page":"69240"},"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9902999997138977,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T11276","display_name":"Solar Radiation and Photovoltaics","score":0.9851999878883362,"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/autoencoder","display_name":"Autoencoder","score":0.7773609161376953},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7206413745880127},{"id":"https://openalex.org/keywords/photovoltaic-system","display_name":"Photovoltaic system","score":0.699301540851593},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.6451598405838013},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4867829978466034},{"id":"https://openalex.org/keywords/electricity-generation","display_name":"Electricity generation","score":0.4684358239173889},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"Unsupervised learning","score":0.42154091596603394},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.39224544167518616},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3813410699367523},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3674946129322052},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3491417169570923},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3324350416660309},{"id":"https://openalex.org/keywords/power","display_name":"Power (physics)","score":0.2996739149093628},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.11480411887168884}],"concepts":[{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.7773609161376953},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7206413745880127},{"id":"https://openalex.org/C41291067","wikidata":"https://www.wikidata.org/wiki/Q1897785","display_name":"Photovoltaic system","level":2,"score":0.699301540851593},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.6451598405838013},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4867829978466034},{"id":"https://openalex.org/C423512","wikidata":"https://www.wikidata.org/wiki/Q383973","display_name":"Electricity generation","level":3,"score":0.4684358239173889},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.42154091596603394},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.39224544167518616},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3813410699367523},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3674946129322052},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3491417169570923},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3324350416660309},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.2996739149093628},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.11480411887168884},{"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/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","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":3,"locations":[{"id":"doi:10.1109/access.2023.3292516","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2023.3292516","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10173524.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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 Access","raw_type":"journal-article"},{"id":"pmh:oai:re.public.polimi.it:11311/1248723","is_oa":true,"landing_page_url":"https://hdl.handle.net/11311/1248723","pdf_url":"https://re.public.polimi.it/bitstream/11311/1248723/1/Knowledge_Extraction_From_PV_Power_Generation_With_Deep_Learning_Autoencoder_and_Clustering-Based_Algorithms.pdf","source":{"id":"https://openalex.org/S4306400312","display_name":"Virtual Community of Pathological Anatomy (University of Castilla La Mancha)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I79189158","host_organization_name":"University of Castilla-La Mancha","host_organization_lineage":["https://openalex.org/I79189158"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"info:eu-repo/semantics/article"},{"id":"pmh:oai:doaj.org/article:28a324d65f9740ac8695deaf61dbe449","is_oa":true,"landing_page_url":"https://doaj.org/article/28a324d65f9740ac8695deaf61dbe449","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","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":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 11, Pp 69227-69240 (2023)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2023.3292516","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2023.3292516","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10173524.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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 Access","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Affordable and clean energy","score":0.8799999952316284,"id":"https://metadata.un.org/sdg/7"}],"awards":[{"id":"https://openalex.org/G3478515953","display_name":null,"funder_award_id":"CN00000023","funder_id":"https://openalex.org/F4320320300","funder_display_name":"European Commission"},{"id":"https://openalex.org/G8455836145","display_name":null,"funder_award_id":"D.D. 1033 17/06/2022, CN00000023","funder_id":"https://openalex.org/F4320320300","funder_display_name":"European Commission"},{"id":"https://openalex.org/G8829736464","display_name":null,"funder_award_id":"1033 17/06/2022","funder_id":"https://openalex.org/F4320320300","funder_display_name":"European Commission"}],"funders":[{"id":"https://openalex.org/F4320320300","display_name":"European Commission","ror":"https://ror.org/00k4n6c32"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4383220037.pdf","grobid_xml":"https://content.openalex.org/works/W4383220037.grobid-xml"},"referenced_works_count":68,"referenced_works":["https://openalex.org/W1894414046","https://openalex.org/W1959608418","https://openalex.org/W2058755023","https://openalex.org/W2071884028","https://openalex.org/W2097747115","https://openalex.org/W2117604780","https://openalex.org/W2163598528","https://openalex.org/W2187089797","https://openalex.org/W2395402165","https://openalex.org/W2604269166","https://openalex.org/W2641506588","https://openalex.org/W2800423051","https://openalex.org/W2808016673","https://openalex.org/W2891838569","https://openalex.org/W2891999340","https://openalex.org/W2897098291","https://openalex.org/W2904810330","https://openalex.org/W2906824711","https://openalex.org/W2911642618","https://openalex.org/W2911773667","https://openalex.org/W2912829604","https://openalex.org/W2917942954","https://openalex.org/W2921934006","https://openalex.org/W2936565617","https://openalex.org/W2943227802","https://openalex.org/W2945930760","https://openalex.org/W2948978827","https://openalex.org/W2951899336","https://openalex.org/W2995268793","https://openalex.org/W3009377873","https://openalex.org/W3010696906","https://openalex.org/W3019747185","https://openalex.org/W3022013598","https://openalex.org/W3028695255","https://openalex.org/W3034919568","https://openalex.org/W3035965352","https://openalex.org/W3041020092","https://openalex.org/W3041342006","https://openalex.org/W3048451018","https://openalex.org/W3091866490","https://openalex.org/W3095263470","https://openalex.org/W3099878876","https://openalex.org/W3100987608","https://openalex.org/W3108350378","https://openalex.org/W3117614253","https://openalex.org/W3131860561","https://openalex.org/W3164387821","https://openalex.org/W3174447828","https://openalex.org/W3187057914","https://openalex.org/W3192916918","https://openalex.org/W3214814497","https://openalex.org/W4214859756","https://openalex.org/W4214958790","https://openalex.org/W4225149814","https://openalex.org/W4281251422","https://openalex.org/W4285270659","https://openalex.org/W4289259876","https://openalex.org/W4289713663","https://openalex.org/W4293370751","https://openalex.org/W4294068707","https://openalex.org/W4316660281","https://openalex.org/W4321371496","https://openalex.org/W4323349207","https://openalex.org/W6640963894","https://openalex.org/W6684327724","https://openalex.org/W6739658937","https://openalex.org/W6748713143","https://openalex.org/W6779475065"],"related_works":["https://openalex.org/W3123344745","https://openalex.org/W2784313445","https://openalex.org/W2292254049","https://openalex.org/W4310034804","https://openalex.org/W3217300629","https://openalex.org/W2592385986","https://openalex.org/W4312783963","https://openalex.org/W4302303815","https://openalex.org/W3208584567","https://openalex.org/W3192794374"],"abstract_inverted_index":{"The":[0,72,143,155],"unpredictable":[1],"nature":[2],"of":[3,35,60,92,161,200],"photovoltaic":[4,52,81],"solar":[5,27],"power":[6,28,53,82,96,192,220],"generation,":[7],"caused":[8],"by":[9,85,107],"changing":[10],"weather":[11,181],"conditions,":[12,182],"creates":[13],"challenges":[14],"for":[15,152],"grid":[16],"operators":[17],"as":[18,184,216],"they":[19],"work":[20],"to":[21,30,56,77,122,135,148,179,210],"balance":[22],"supply":[23],"and":[24,173,190,231,241],"demand.":[25],"As":[26],"continues":[29],"become":[31],"a":[32,99,108,130],"larger":[33],"part":[34],"the":[36,61,65,87,93,113,124,162,201],"energy":[37,228,235],"mix,":[38],"managing":[39],"this":[40],"intermittency":[41],"will":[42],"be":[43,208,211],"increasingly":[44],"important.":[45],"This":[46],"paper":[47],"focuses":[48],"on":[49,68],"identifying":[50],"daily":[51,80,94],"production":[54],"patterns":[55,63,84,138,164,177,206,222],"gain":[57],"new":[58,131],"knowledge":[59],"generation":[62,83,193,221],"throughout":[64],"year":[66],"based":[67],"unsupervised":[69],"learning":[70,110],"algorithms.":[71],"proposed":[73,134,144],"data-driven":[74],"model":[75,145],"aims":[76],"extract":[78],"typical":[79,205],"transforming":[86],"high":[88,169],"dimensional":[89],"temporal":[90,174],"features":[91],"PV":[95,219],"output":[97],"into":[98],"lower":[100],"latent":[101],"feature":[102],"space,":[103],"which":[104,195],"is":[105,120,133,146],"learned":[106],"deep":[109],"autoencoder.":[111],"Subsequently,":[112],"Partitioning":[114],"Around":[115],"Medoids":[116],"(PAM)":[117],"clustering":[118],"algorithm":[119,132],"employed":[121],"identify":[123],"six":[125],"distinct":[126,150,180],"dominant":[127],"patterns.":[128],"Finally,":[129],"reconstruct":[136],"these":[137],"in":[139,165,213,233],"their":[140],"original":[141],"subspace.":[142],"applied":[147],"two":[149],"datasets":[151,167],"further":[153],"analysis.":[154],"results":[156],"indicate":[157],"that":[158],"four":[159],"out":[160],"identified":[163],"both":[166],"exhibit":[168],"correlation":[170],"(over":[171],"95%)":[172],"trends.":[175],"These":[176,204],"correspond":[178],"such":[183],"entirely":[185],"sunny,":[186,188],"mostly":[187],"cloudy,":[189],"negligible":[191],"days,":[194],"were":[196],"observed":[197,212],"approximately":[198],"61%":[199],"analyzed":[202],"period.":[203],"can":[207,223],"expected":[209],"other":[214],"locations":[215],"well.":[217],"Identified":[218],"improve":[224],"forecasting":[225],"models,":[226],"optimize":[227],"management":[229],"systems,":[230],"aid":[232],"implementing":[234],"storage":[236],"or":[237],"demand":[238],"response":[239],"programs":[240],"scheduling":[242],"efficiently.":[243]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":7},{"year":2024,"cited_by_count":13},{"year":2023,"cited_by_count":3}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}
