{"id":"https://openalex.org/W3047034125","doi":"https://doi.org/10.1109/ijcnn48605.2020.9207573","title":"Forecasting Photovoltaic Power Production using a Deep Learning Sequence to Sequence Model with Attention","display_name":"Forecasting Photovoltaic Power Production using a Deep Learning Sequence to Sequence Model with Attention","publication_year":2020,"publication_date":"2020-07-01","ids":{"openalex":"https://openalex.org/W3047034125","doi":"https://doi.org/10.1109/ijcnn48605.2020.9207573","mag":"3047034125"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn48605.2020.9207573","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn48605.2020.9207573","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2008.02775","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Elizaveta Kharlova","orcid":null},"institutions":[{"id":"https://openalex.org/I154425047","display_name":"University of Alberta","ror":"https://ror.org/0160cpw27","country_code":"CA","type":"education","lineage":["https://openalex.org/I154425047"]}],"countries":["CA"],"is_corresponding":true,"raw_author_name":"Elizaveta Kharlova","raw_affiliation_strings":["Electrical and Computer Engineering, University of Alberta, Edmonton, Canada"],"affiliations":[{"raw_affiliation_string":"Electrical and Computer Engineering, University of Alberta, Edmonton, Canada","institution_ids":["https://openalex.org/I154425047"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Daniel May","orcid":null},"institutions":[{"id":"https://openalex.org/I154425047","display_name":"University of Alberta","ror":"https://ror.org/0160cpw27","country_code":"CA","type":"education","lineage":["https://openalex.org/I154425047"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Daniel May","raw_affiliation_strings":["Electrical and Computer Engineering, University of Alberta, Edmonton, Canada"],"affiliations":[{"raw_affiliation_string":"Electrical and Computer Engineering, University of Alberta, Edmonton, Canada","institution_ids":["https://openalex.org/I154425047"]}]},{"author_position":"last","author":{"id":null,"display_name":"Petr Musilek","orcid":null},"institutions":[{"id":"https://openalex.org/I154425047","display_name":"University of Alberta","ror":"https://ror.org/0160cpw27","country_code":"CA","type":"education","lineage":["https://openalex.org/I154425047"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Petr Musilek","raw_affiliation_strings":["Electrical and Computer Engineering, University of Alberta, Edmonton, Canada"],"affiliations":[{"raw_affiliation_string":"Electrical and Computer Engineering, University of Alberta, Edmonton, Canada","institution_ids":["https://openalex.org/I154425047"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I154425047"],"apc_list":null,"apc_paid":null,"fwci":0.9599,"has_fulltext":false,"cited_by_count":16,"citation_normalized_percentile":{"value":0.81114142,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":93,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11276","display_name":"Solar Radiation and Photovoltaics","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9968000054359436,"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":0.9955000281333923,"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/deep-learning","display_name":"Deep learning","score":0.676800012588501},{"id":"https://openalex.org/keywords/photovoltaic-system","display_name":"Photovoltaic system","score":0.6136999726295471},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5156000256538391},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.4993000030517578},{"id":"https://openalex.org/keywords/sequence","display_name":"Sequence (biology)","score":0.4805999994277954},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.4771000146865845},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.45559999346733093},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.4099999964237213},{"id":"https://openalex.org/keywords/power","display_name":"Power (physics)","score":0.3702000081539154}],"concepts":[{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.676800012588501},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6141999959945679},{"id":"https://openalex.org/C41291067","wikidata":"https://www.wikidata.org/wiki/Q1897785","display_name":"Photovoltaic system","level":2,"score":0.6136999726295471},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5586000084877014},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5156000256538391},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.4993000030517578},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.4805999994277954},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.4771000146865845},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.45559999346733093},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4478999972343445},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.4099999964237213},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.3702000081539154},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.36579999327659607},{"id":"https://openalex.org/C206658404","wikidata":"https://www.wikidata.org/wiki/Q12725","display_name":"Electricity","level":2,"score":0.33550000190734863},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.32519999146461487},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.32010000944137573},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.3158000111579895},{"id":"https://openalex.org/C3020136221","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time sequence","level":2,"score":0.3107999861240387},{"id":"https://openalex.org/C2778348673","wikidata":"https://www.wikidata.org/wiki/Q739302","display_name":"Production (economics)","level":2,"score":0.29440000653266907},{"id":"https://openalex.org/C89227174","wikidata":"https://www.wikidata.org/wiki/Q2388981","display_name":"Electric power system","level":3,"score":0.289900004863739},{"id":"https://openalex.org/C423512","wikidata":"https://www.wikidata.org/wiki/Q383973","display_name":"Electricity generation","level":3,"score":0.2847999930381775},{"id":"https://openalex.org/C193809577","wikidata":"https://www.wikidata.org/wiki/Q3409300","display_name":"Demand forecasting","level":2,"score":0.2833999991416931},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2782000005245209},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.2671000063419342},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.2621999979019165},{"id":"https://openalex.org/C188573790","wikidata":"https://www.wikidata.org/wiki/Q12705","display_name":"Renewable energy","level":2,"score":0.2581999897956848},{"id":"https://openalex.org/C122282355","wikidata":"https://www.wikidata.org/wiki/Q7246855","display_name":"Probabilistic forecasting","level":3,"score":0.2572999894618988},{"id":"https://openalex.org/C40506919","wikidata":"https://www.wikidata.org/wiki/Q7452469","display_name":"Sequence learning","level":2,"score":0.2542000114917755}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/ijcnn48605.2020.9207573","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn48605.2020.9207573","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2008.02775","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2008.02775","pdf_url":"https://arxiv.org/pdf/2008.02775","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"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":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2008.02775","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2008.02775","pdf_url":"https://arxiv.org/pdf/2008.02775","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"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":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W1902237438","https://openalex.org/W2064675550","https://openalex.org/W2341563681","https://openalex.org/W2346662913","https://openalex.org/W2469734051","https://openalex.org/W2569349941","https://openalex.org/W2622052728","https://openalex.org/W2751698537","https://openalex.org/W2773629498","https://openalex.org/W2910890149","https://openalex.org/W2941419477","https://openalex.org/W2971341457","https://openalex.org/W2976858096","https://openalex.org/W6640212811","https://openalex.org/W6679434410","https://openalex.org/W6679436768","https://openalex.org/W6717377403","https://openalex.org/W6739901393"],"related_works":[],"abstract_inverted_index":{"Rising":[0],"penetration":[1],"levels":[2],"of":[3,15,29,48,67,81,124,187,190],"(residential)":[4],"photovoltaic":[5],"(PV)":[6],"power":[7,30,50,192],"as":[8,93,140,161],"distributed":[9],"energy":[10],"resource":[11],"pose":[12],"a":[13,40,94,110,162],"number":[14],"challenges":[16],"to":[17,25,63,87,108,135,170],"the":[18,79,85,115,121,125,165,177,184,188],"electricity":[19],"infrastructure.":[20],"High":[21],"quality,":[22],"general":[23],"tools":[24],"provide":[26],"accurate":[27],"forecasts":[28],"production":[31],"are":[32],"urgently":[33],"needed.":[34],"In":[35],"this":[36],"article,":[37],"we":[38],"propose":[39],"supervised":[41],"deep":[42,68],"learning":[43,69],"model":[44,54,99],"for":[45],"end-to-end":[46],"forecasting":[47],"PV":[49,191],"production.":[51],"The":[52,97,173],"proposed":[53,98,166],"is":[55,168],"based":[56,157],"on":[57],"two":[58],"seminal":[59],"concepts":[60],"that":[61,176],"led":[62],"significant":[64,131],"performance":[65,132,163],"improvements":[66,133],"approaches":[70],"in":[71,78],"other":[72,171],"sequence-related":[73],"fields,":[74],"but":[75],"not":[76],"yet":[77],"area":[80],"time":[82,117],"series":[83],"prediction:":[84],"sequence":[86,88],"architecture":[89],"and":[90,104,145],"attention":[91],"mechanism":[92],"context":[95],"generator.":[96],"leverages":[100],"numerical":[101],"weather":[102],"predictions":[103],"high-resolution":[105],"historical":[106],"measurements":[107],"forecast":[109,158],"binned":[111],"probability":[112],"distribution":[113],"over":[114],"prognostic":[116,126],"intervals,":[118],"rather":[119],"than":[120],"expected":[122],"values":[123],"variable.":[127],"This":[128],"design":[129,179],"offers":[130],"compared":[134,169],"common":[136],"baseline":[137],"approaches,":[138],"such":[139],"fully":[141],"connected":[142],"neural":[143],"networks":[144],"one-block":[146],"long":[147],"short-term":[148],"memory":[149],"architectures.":[150],"Using":[151],"normalized":[152],"root":[153],"mean":[154],"square":[155],"error":[156],"skill":[159],"score":[160],"indicator,":[164],"approach":[167],"models.":[172],"results":[174],"show":[175],"new":[178],"performs":[180],"at":[181],"or":[182],"above":[183],"current":[185],"state":[186],"art":[189],"forecasting.":[193]},"counts_by_year":[{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":2}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2020-08-10T00:00:00"}
