{"id":"https://openalex.org/W3021650932","doi":"https://doi.org/10.1109/isgt45199.2020.9087786","title":"Short-Term Photovoltaic Power Forecasting Using an LSTM Neural Network","display_name":"Short-Term Photovoltaic Power Forecasting Using an LSTM Neural Network","publication_year":2020,"publication_date":"2020-02-01","ids":{"openalex":"https://openalex.org/W3021650932","doi":"https://doi.org/10.1109/isgt45199.2020.9087786","mag":"3021650932"},"language":"en","primary_location":{"id":"doi:10.1109/isgt45199.2020.9087786","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isgt45199.2020.9087786","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE Power &amp; Energy Society Innovative Smart Grid Technologies Conference (ISGT)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5059757110","display_name":"Mohammad Safayet Hossain","orcid":"https://orcid.org/0000-0002-6745-4168"},"institutions":[{"id":"https://openalex.org/I32480017","display_name":"Florida Polytechnic University","ror":"https://ror.org/01e5mdj42","country_code":"US","type":"education","lineage":["https://openalex.org/I32480017"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mohammad Safayet Hossain","raw_affiliation_strings":["Florida Polytechnic University,Department of Electrical and Computer Engineering,Lakeland,FL,USA","Department of Electrical and Computer Engineering, Florida Polytechnic University, Lakeland, FL, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Florida Polytechnic University,Department of Electrical and Computer Engineering,Lakeland,FL,USA","institution_ids":["https://openalex.org/I32480017"]},{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Florida Polytechnic University, Lakeland, FL, USA","institution_ids":["https://openalex.org/I32480017"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5023654698","display_name":"Hisham Mahmood","orcid":"https://orcid.org/0000-0003-2400-7154"},"institutions":[{"id":"https://openalex.org/I32480017","display_name":"Florida Polytechnic University","ror":"https://ror.org/01e5mdj42","country_code":"US","type":"education","lineage":["https://openalex.org/I32480017"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hisham Mahmood","raw_affiliation_strings":["Florida Polytechnic University,Department of Electrical and Computer Engineering,Lakeland,FL,USA","Department of Electrical and Computer Engineering, Florida Polytechnic University, Lakeland, FL, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Florida Polytechnic University,Department of Electrical and Computer Engineering,Lakeland,FL,USA","institution_ids":["https://openalex.org/I32480017"]},{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Florida Polytechnic University, Lakeland, FL, USA","institution_ids":["https://openalex.org/I32480017"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I32480017"],"apc_list":null,"apc_paid":null,"fwci":1.265,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.83442939,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"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/T11052","display_name":"Energy Load and Power Forecasting","score":0.9998999834060669,"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":0.9983000159263611,"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/nonlinear-autoregressive-exogenous-model","display_name":"Nonlinear autoregressive exogenous model","score":0.8247263431549072},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.7895523905754089},{"id":"https://openalex.org/keywords/autoregressive-model","display_name":"Autoregressive model","score":0.6797035932540894},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6789178252220154},{"id":"https://openalex.org/keywords/photovoltaic-system","display_name":"Photovoltaic system","score":0.6112727522850037},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.5521939992904663},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.5511878132820129},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5056154727935791},{"id":"https://openalex.org/keywords/power","display_name":"Power (physics)","score":0.5004932880401611},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.4333701431751251},{"id":"https://openalex.org/keywords/nonlinear-system","display_name":"Nonlinear system","score":0.4252581298351288},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.41538187861442566},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.41450393199920654},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.17181885242462158},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1305837333202362},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.11260896921157837}],"concepts":[{"id":"https://openalex.org/C42536954","wikidata":"https://www.wikidata.org/wiki/Q7049462","display_name":"Nonlinear autoregressive exogenous model","level":3,"score":0.8247263431549072},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.7895523905754089},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.6797035932540894},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6789178252220154},{"id":"https://openalex.org/C41291067","wikidata":"https://www.wikidata.org/wiki/Q1897785","display_name":"Photovoltaic system","level":2,"score":0.6112727522850037},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.5521939992904663},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.5511878132820129},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5056154727935791},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.5004932880401611},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.4333701431751251},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.4252581298351288},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.41538187861442566},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.41450393199920654},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.17181885242462158},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1305837333202362},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.11260896921157837},{"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/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":1,"locations":[{"id":"doi:10.1109/isgt45199.2020.9087786","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isgt45199.2020.9087786","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE Power &amp; Energy Society Innovative Smart Grid Technologies Conference (ISGT)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy","score":0.8899999856948853}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W832636891","https://openalex.org/W2088786192","https://openalex.org/W2197589148","https://openalex.org/W2525448601","https://openalex.org/W2754252319","https://openalex.org/W2769930725","https://openalex.org/W2888449021","https://openalex.org/W2894793845","https://openalex.org/W2900404805","https://openalex.org/W2903265999","https://openalex.org/W2912623183","https://openalex.org/W2949062675"],"related_works":["https://openalex.org/W2606910468","https://openalex.org/W3116827148","https://openalex.org/W3120843198","https://openalex.org/W2799656149","https://openalex.org/W2154965898","https://openalex.org/W2036704594","https://openalex.org/W4226315710","https://openalex.org/W3083782034","https://openalex.org/W4287185323","https://openalex.org/W2117809914"],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"two":[3],"algorithms":[4],"are":[5,120],"proposed":[6,139],"for":[7,52],"short-term":[8,15],"PV":[9,32],"power":[10],"forecasting":[11,40,130],"using":[12],"a":[13,28],"long":[14],"memory":[16],"(LSTM)":[17],"neural":[18,147,153,162],"network":[19,154],"(NN).":[20],"The":[21,59,75],"first":[22],"algorithm":[23],"is":[24,37,73,82,92,164],"designed":[25],"to":[26,122,133],"predict":[27],"single":[29],"step":[30],"ahead":[31],"power,":[33],"whereas":[34],"the":[35,62,67,70,79,96,109,126,129,135,138,143,150,157],"latter":[36],"capable":[38],"of":[39,61,69,78,87,98,128,137,145],"time":[41],"horizons":[42],"with":[43,84],"variable":[44],"starting":[45],"points,":[46],"which":[47],"makes":[48],"it":[49],"very":[50],"useful":[51],"rolling":[53,88,100],"horizon":[54],"based":[55,141],"energy":[56],"management":[57],"algorithms.":[58],"effect":[60],"input":[63],"sequence":[64],"length":[65],"on":[66],"performance":[68,127,144],"single-step":[71],"model":[72,81],"investigated.":[74],"prediction":[76,89],"accuracy":[77],"multi-step":[80],"examined":[83],"different":[85,118],"lengths":[86],"horizons.":[90],"It":[91],"shown":[93],"that":[94],"in":[95],"case":[97],"intraday":[99],"horizons,":[101],"adding":[102],"certain":[103],"new":[104],"predictors":[105],"can":[106],"effectively":[107],"improve":[108],"machine":[110],"performance.":[111],"Hourly":[112],"and":[113,124,156],"half":[114],"hourly":[115],"data":[116],"from":[117],"seasons":[119],"used":[121],"train":[123],"test":[125],"machine.":[131],"Moreover,":[132],"demonstrate":[134],"superiority":[136],"LSTM":[140],"algorithms,":[142],"other":[146],"networks,":[148],"namely":[149],"generalized":[151],"recurrent":[152],"(GRNN)":[155],"nonlinear":[158],"autoregressive":[159],"exogenous":[160],"(NARX)":[161],"network,":[163],"also":[165],"explored.":[166]},"counts_by_year":[{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":2}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
