{"id":"https://openalex.org/W4391094770","doi":"https://doi.org/10.1109/icecet58911.2023.10389351","title":"Modelling Ageing and Power Production of Solar PV Using Machine Learning Techniques","display_name":"Modelling Ageing and Power Production of Solar PV Using Machine Learning Techniques","publication_year":2023,"publication_date":"2023-11-16","ids":{"openalex":"https://openalex.org/W4391094770","doi":"https://doi.org/10.1109/icecet58911.2023.10389351"},"language":"en","primary_location":{"id":"doi:10.1109/icecet58911.2023.10389351","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icecet58911.2023.10389351","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 International Conference on Electrical, Computer and Energy Technologies (ICECET)","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":["DEIB Politecnico di Milano,Milano,Italy","DEIB Politecnico di Milano, Milano, Italy"],"affiliations":[{"raw_affiliation_string":"DEIB Politecnico di Milano,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":["DEIB Politecnico di Milano,Milano,Italy","DEIB Politecnico di Milano, Milano, Italy"],"affiliations":[{"raw_affiliation_string":"DEIB Politecnico di Milano,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":["DEIB Politecnico di Milano,Milano,Italy","DEIB Politecnico di Milano, Milano, Italy"],"affiliations":[{"raw_affiliation_string":"DEIB Politecnico di Milano,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":0.5882,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.732074,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"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":1.0,"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":1.0,"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/T10468","display_name":"Photovoltaic System Optimization Techniques","score":0.9997000098228455,"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"}},{"id":"https://openalex.org/T11052","display_name":"Energy Load and Power Forecasting","score":0.9994000196456909,"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/computer-science","display_name":"Computer science","score":0.6740202903747559},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6667795181274414},{"id":"https://openalex.org/keywords/photovoltaic-system","display_name":"Photovoltaic system","score":0.6073693633079529},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5987528562545776},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5849605798721313},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5746742486953735},{"id":"https://openalex.org/keywords/data-pre-processing","display_name":"Data pre-processing","score":0.5269202589988708},{"id":"https://openalex.org/keywords/preprocessor","display_name":"Preprocessor","score":0.5149970054626465},{"id":"https://openalex.org/keywords/predictive-modelling","display_name":"Predictive modelling","score":0.45792752504348755},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4559202790260315},{"id":"https://openalex.org/keywords/power","display_name":"Power (physics)","score":0.45261484384536743},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.4378199875354767},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.43728697299957275},{"id":"https://openalex.org/keywords/electricity-generation","display_name":"Electricity generation","score":0.43119096755981445},{"id":"https://openalex.org/keywords/solar-power","display_name":"Solar power","score":0.412309467792511},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.26999443769454956}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6740202903747559},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6667795181274414},{"id":"https://openalex.org/C41291067","wikidata":"https://www.wikidata.org/wiki/Q1897785","display_name":"Photovoltaic system","level":2,"score":0.6073693633079529},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5987528562545776},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5849605798721313},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5746742486953735},{"id":"https://openalex.org/C10551718","wikidata":"https://www.wikidata.org/wiki/Q5227332","display_name":"Data pre-processing","level":2,"score":0.5269202589988708},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.5149970054626465},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.45792752504348755},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4559202790260315},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.45261484384536743},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.4378199875354767},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.43728697299957275},{"id":"https://openalex.org/C423512","wikidata":"https://www.wikidata.org/wiki/Q383973","display_name":"Electricity generation","level":3,"score":0.43119096755981445},{"id":"https://openalex.org/C2777618391","wikidata":"https://www.wikidata.org/wiki/Q1483757","display_name":"Solar power","level":3,"score":0.412309467792511},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.26999443769454956},{"id":"https://openalex.org/C59822182","wikidata":"https://www.wikidata.org/wiki/Q441","display_name":"Botany","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},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","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}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/icecet58911.2023.10389351","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icecet58911.2023.10389351","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 International Conference on Electrical, Computer and Energy Technologies (ICECET)","raw_type":"proceedings-article"},{"id":"pmh:oai:re.public.polimi.it:11311/1262666","is_oa":false,"landing_page_url":"https://hdl.handle.net/11311/1262666","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":[{"display_name":"Affordable and clean energy","score":0.7900000214576721,"id":"https://metadata.un.org/sdg/7"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W1909551488","https://openalex.org/W1966694816","https://openalex.org/W1988989532","https://openalex.org/W2011866185","https://openalex.org/W2298264562","https://openalex.org/W2339636646","https://openalex.org/W2394990251","https://openalex.org/W2471422049","https://openalex.org/W2519746072","https://openalex.org/W2524035252","https://openalex.org/W2549957027","https://openalex.org/W2739420384","https://openalex.org/W2751698537","https://openalex.org/W2763310144","https://openalex.org/W2792764867","https://openalex.org/W2997902256","https://openalex.org/W3009377873","https://openalex.org/W3040756860","https://openalex.org/W3122315025","https://openalex.org/W3153822163","https://openalex.org/W3204359965","https://openalex.org/W4238432077","https://openalex.org/W4313303740","https://openalex.org/W6749825310","https://openalex.org/W6848156183"],"related_works":["https://openalex.org/W2989490741","https://openalex.org/W3092506759","https://openalex.org/W2367545121","https://openalex.org/W4248881655","https://openalex.org/W2482165163","https://openalex.org/W3010890513","https://openalex.org/W120741642","https://openalex.org/W138569904","https://openalex.org/W2390914021","https://openalex.org/W2389417819"],"abstract_inverted_index":{"Solar":[0],"photovoltaic":[1],"(PV)":[2],"power":[3,41,46,60,116,162,185,202],"prediction":[4,56,155],"plays":[5],"a":[6],"pivotal":[7],"role":[8],"in":[9,161,212],"optimizing":[10],"energy":[11,16],"management":[12],"within":[13],"the":[14,23,37,70,88,105,109,127,206],"re-newable":[15],"industry.":[17],"In":[18,157],"this":[19],"investigation,":[20],"we":[21],"explore":[22],"utilization":[24],"of":[25,74,93,111,129,198,208],"artificial":[26],"neural":[27,78,83],"networks":[28],"(ANNs)":[29],"to":[30,66,68,126,182],"model":[31],"solar":[32],"PV":[33,59,115,201],"ageing":[34,113],"and,":[35],"at":[36],"same":[38],"time,":[39],"forecast":[40],"generation.":[42],"Diverse":[43],"factors":[44],"impacting":[45],"output":[47],"are":[48,53,170],"examined,":[49],"and":[50,64,72,81,122,143,146,167,187,204,215],"multiple":[51],"ANNs":[52,199],"explored":[54],"for":[55,150,176,200],"purposes.":[57],"Real-world":[58],"data":[61,123],"is":[62,136,180],"collected":[63],"subjected":[65],"preprocessing":[67],"facilitate":[69],"training":[71],"testing":[73],"ANNs,":[75,94],"including":[76],"recurrent":[77],"networks,":[79],"autoencoders,":[80],"convolutional":[82],"networks.":[84,103],"The":[85,134,192],"findings":[86],"demonstrate":[87],"accurate":[89,214],"short-term":[90],"forecasting":[91,203],"capa-bilities":[92],"with":[95],"particular":[96],"emphasis":[97],"on":[98,114],"Long":[99],"Short-term":[100],"Memory":[101],"(LSTM)":[102],"Additionally,":[104],"study":[106],"delves":[107],"into":[108,139],"effects":[110],"panel":[112],"by":[117],"leveraging":[118],"machine":[119,209],"learning":[120,210],"models":[121,149,175],"analysis,":[124],"leading":[125],"identification":[128],"effective":[130],"performance":[131],"degradation":[132],"prediction.":[133],"dataset":[135],"further":[137],"segmented":[138],"subsets":[140],"representing":[141],"sunny":[142,166],"cloudy":[144,168],"conditions,":[145],"employing":[147],"separate":[148],"each":[151],"subset":[152],"yields":[153],"improved":[154],"accuracy.":[156],"fact,":[158],"notable":[159],"distinctions":[160],"production":[163],"characteristics":[164],"between":[165],"con-ditions":[169],"revealed.":[171],"Thus,":[172],"tailoring":[173],"distinct":[174],"different":[177],"weather":[178],"conditions":[179],"crucial":[181],"ensure":[183],"precise":[184],"predictions":[186],"effectively":[188],"address":[189],"daily":[190],"uncertainties.":[191],"research":[193],"presents":[194],"an":[195],"extensive":[196],"analysis":[197],"emphasizes":[205],"potential":[207],"techniques":[211],"enabling":[213],"reliable":[216],"predictions.":[217]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}