{"id":"https://openalex.org/W4388726505","doi":"https://doi.org/10.1109/iecon51785.2023.10312446","title":"Solar PV Power Forecasting and Ageing Evaluation Using Machine Learning Techniques","display_name":"Solar PV Power Forecasting and Ageing Evaluation Using Machine Learning Techniques","publication_year":2023,"publication_date":"2023-10-16","ids":{"openalex":"https://openalex.org/W4388726505","doi":"https://doi.org/10.1109/iecon51785.2023.10312446"},"language":"en","primary_location":{"id":"doi:10.1109/iecon51785.2023.10312446","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iecon51785.2023.10312446","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IECON 2023- 49th 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/A5065370380","display_name":"Saloni Dhingra","orcid":null},"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":true,"raw_author_name":"Saloni Dhingra","raw_affiliation_strings":["Politecnico di Milano,DEIB,Milano,Italy","DEIB, Politecnico di Milano, Milano, Italy"],"affiliations":[{"raw_affiliation_string":"Politecnico di Milano,DEIB,Milano,Italy","institution_ids":["https://openalex.org/I93860229"]},{"raw_affiliation_string":"DEIB, Politecnico di Milano, Milano, Italy","institution_ids":["https://openalex.org/I93860229"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032756237","display_name":"Giambattista Gruosso","orcid":"https://orcid.org/0000-0001-6417-3750"},"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":"Giambattista Gruosso","raw_affiliation_strings":["Politecnico di Milano,DEIB,Milano,Italy","DEIB, Politecnico di Milano, Milano, Italy"],"affiliations":[{"raw_affiliation_string":"Politecnico di Milano,DEIB,Milano,Italy","institution_ids":["https://openalex.org/I93860229"]},{"raw_affiliation_string":"DEIB, Politecnico di Milano, Milano, Italy","institution_ids":["https://openalex.org/I93860229"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5064664899","display_name":"Giancarlo Storti Gajani","orcid":"https://orcid.org/0000-0001-8182-7891"},"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":"Giancarlo Storti Gajani","raw_affiliation_strings":["Politecnico di Milano,DEIB,Milano,Italy","DEIB, Politecnico di Milano, Milano, Italy"],"affiliations":[{"raw_affiliation_string":"Politecnico di Milano,DEIB,Milano,Italy","institution_ids":["https://openalex.org/I93860229"]},{"raw_affiliation_string":"DEIB, Politecnico di Milano, Milano, Italy","institution_ids":["https://openalex.org/I93860229"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5065370380"],"corresponding_institution_ids":["https://openalex.org/I93860229"],"apc_list":null,"apc_paid":null,"fwci":1.3986,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.85331133,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"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/T11276","display_name":"Solar Radiation and Photovoltaics","score":0.9998000264167786,"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"}},"topics":[{"id":"https://openalex.org/T11276","display_name":"Solar Radiation and Photovoltaics","score":0.9998000264167786,"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/T11052","display_name":"Energy Load and Power Forecasting","score":0.9997000098228455,"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/T10468","display_name":"Photovoltaic System Optimization Techniques","score":0.9969000220298767,"subfield":{"id":"https://openalex.org/subfields/2105","display_name":"Renewable Energy, Sustainability and the Environment"},"field":{"id":"https://openalex.org/fields/21","display_name":"Energy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.7141395807266235},{"id":"https://openalex.org/keywords/photovoltaic-system","display_name":"Photovoltaic system","score":0.7017543911933899},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.63312166929245},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.5765841007232666},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5311689972877502},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5298784971237183},{"id":"https://openalex.org/keywords/renewable-energy","display_name":"Renewable energy","score":0.5212700366973877},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.49333110451698303},{"id":"https://openalex.org/keywords/power","display_name":"Power (physics)","score":0.46621328592300415},{"id":"https://openalex.org/keywords/solar-power","display_name":"Solar power","score":0.41702917218208313},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.4131150245666504},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.2743944525718689},{"id":"https://openalex.org/keywords/electrical-engineering","display_name":"Electrical engineering","score":0.0666361153125763}],"concepts":[{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.7141395807266235},{"id":"https://openalex.org/C41291067","wikidata":"https://www.wikidata.org/wiki/Q1897785","display_name":"Photovoltaic system","level":2,"score":0.7017543911933899},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.63312166929245},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.5765841007232666},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5311689972877502},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5298784971237183},{"id":"https://openalex.org/C188573790","wikidata":"https://www.wikidata.org/wiki/Q12705","display_name":"Renewable energy","level":2,"score":0.5212700366973877},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.49333110451698303},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.46621328592300415},{"id":"https://openalex.org/C2777618391","wikidata":"https://www.wikidata.org/wiki/Q1483757","display_name":"Solar power","level":3,"score":0.41702917218208313},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.4131150245666504},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.2743944525718689},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0666361153125763},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/iecon51785.2023.10312446","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iecon51785.2023.10312446","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IECON 2023- 49th Annual Conference of the IEEE Industrial Electronics Society","raw_type":"proceedings-article"},{"id":"pmh:oai:re.public.polimi.it:11311/1258371","is_oa":false,"landing_page_url":"https://hdl.handle.net/11311/1258371","pdf_url":null,"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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"info:eu-repo/semantics/conferenceObject"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6399999856948853,"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W1909551488","https://openalex.org/W2030474209","https://openalex.org/W2064675550","https://openalex.org/W2107878631","https://openalex.org/W2339636646","https://openalex.org/W2394990251","https://openalex.org/W2519746072","https://openalex.org/W2524035252","https://openalex.org/W2549957027","https://openalex.org/W2739420384","https://openalex.org/W2760948241","https://openalex.org/W2763310144","https://openalex.org/W2792764867","https://openalex.org/W2895224823","https://openalex.org/W3009377873","https://openalex.org/W3122315025","https://openalex.org/W3128276472","https://openalex.org/W4226449636","https://openalex.org/W4313303740","https://openalex.org/W4316928279","https://openalex.org/W6749825310","https://openalex.org/W6848156183"],"related_works":["https://openalex.org/W2386968573","https://openalex.org/W2395064349","https://openalex.org/W2034374297","https://openalex.org/W4293226380","https://openalex.org/W2382628689","https://openalex.org/W2766130412","https://openalex.org/W2526730640","https://openalex.org/W2351171996","https://openalex.org/W2983370139","https://openalex.org/W4247798368"],"abstract_inverted_index":{"Solar":[0],"photovoltaic":[1],"(PV)":[2],"power":[3,32,39,49,83,100,139],"forecasting":[4,140],"is":[5,51],"a":[6,132],"crucial":[7],"aspect":[8],"of":[9,23,95,108,135,144,150],"efficient":[10],"energy":[11,16],"management":[12],"in":[13,85],"the":[14,21,86,93,112,142,148],"renewable":[15],"sector.":[17],"This":[18],"study":[19,90,130],"examines":[20],"use":[22],"artificial":[24],"neural":[25,63,68],"networks":[26],"(ANNs)":[27],"to":[28],"forecast":[29,81],"solar":[30],"PV":[31,48,82,99,138],"output.":[33],"It":[34],"considers":[35],"various":[36],"factors":[37],"influencing":[38],"output":[40,84],"and":[41,53,57,66,116,119,141,155],"investigates":[42],"different":[43],"ANNs":[44,59,136],"for":[45,55,123,137,153],"prediction.":[46],"Real-world":[47],"data":[50],"collected":[52],"preprocessed":[54],"training":[56],"testing":[58],"such":[60],"as":[61],"recurrent":[62],"networks,":[64],"autoencoders,":[65],"convolutional":[67],"networks.":[69],"The":[70,89,129],"results":[71],"show":[72],"that":[73],"ANNs,":[74],"particularly":[75],"Long":[76],"Short-term":[77],"memory":[78],"(LSTM),":[79],"accurately":[80],"short":[87],"term.":[88],"also":[91],"analyzes":[92],"impact":[94],"panel":[96,145],"ageing":[97],"on":[98],"using":[101,120],"machine":[102,151],"learning":[103,152],"models,":[104],"revealing":[105],"effective":[106],"prediction":[107,127],"performance":[109],"degradation.":[110],"Clustering":[111],"dataset":[113],"into":[114],"sunny":[115],"cloudy":[117],"subsets,":[118],"separate":[121],"models":[122],"each":[124],"subset":[125],"improves":[126],"accuracy.":[128],"presents":[131],"comprehensive":[133],"analysis":[134],"influence":[143],"ageing,":[146],"highlighting":[147],"potential":[149],"precise":[154],"reliable":[156],"predictions.":[157]},"counts_by_year":[{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
