{"id":"https://openalex.org/W2995084088","doi":"https://doi.org/10.1109/iecon.2019.8926801","title":"Development of a Forecasting Module based on Tensorflow for Use in Energy Management Systems","display_name":"Development of a Forecasting Module based on Tensorflow for Use in Energy Management Systems","publication_year":2019,"publication_date":"2019-10-01","ids":{"openalex":"https://openalex.org/W2995084088","doi":"https://doi.org/10.1109/iecon.2019.8926801","mag":"2995084088"},"language":"en","primary_location":{"id":"doi:10.1109/iecon.2019.8926801","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iecon.2019.8926801","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IECON 2019 - 45th 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/A5057196257","display_name":"Giuseppe La Tona","orcid":"https://orcid.org/0000-0002-9097-6626"},"institutions":[{"id":"https://openalex.org/I4210133906","display_name":"Institute of Marine Engineering","ror":"https://ror.org/02qnx8e75","country_code":"IT","type":"education","lineage":["https://openalex.org/I4210133906","https://openalex.org/I4210155236"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Giuseppe La Tona","raw_affiliation_strings":["Istituto di Ingegneria del Mare (INM)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Istituto di Ingegneria del Mare (INM)","institution_ids":["https://openalex.org/I4210133906"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009333089","display_name":"Massimiliano Luna","orcid":"https://orcid.org/0000-0001-8900-9367"},"institutions":[{"id":"https://openalex.org/I4210133906","display_name":"Institute of Marine Engineering","ror":"https://ror.org/02qnx8e75","country_code":"IT","type":"education","lineage":["https://openalex.org/I4210133906","https://openalex.org/I4210155236"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Massimiliano Luna","raw_affiliation_strings":["Istituto di Ingegneria del Mare (INM)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Istituto di Ingegneria del Mare (INM)","institution_ids":["https://openalex.org/I4210133906"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103086956","display_name":"Annalisa Di Piazza","orcid":"https://orcid.org/0000-0002-3221-2986"},"institutions":[{"id":"https://openalex.org/I4210144062","display_name":"Consorzio Roma Ricerche","ror":"https://ror.org/03jvpn714","country_code":"IT","type":"nonprofit","lineage":["https://openalex.org/I4210144062"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Annalisa di Piazza","raw_affiliation_strings":["Consiglio Nazionale delle Ricerche (CNR), Palermo, Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Consiglio Nazionale delle Ricerche (CNR), Palermo, Italy","institution_ids":["https://openalex.org/I4210144062"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5112935736","display_name":"Maria Carmela Di Piazza","orcid":null},"institutions":[{"id":"https://openalex.org/I4210144062","display_name":"Consorzio Roma Ricerche","ror":"https://ror.org/03jvpn714","country_code":"IT","type":"nonprofit","lineage":["https://openalex.org/I4210144062"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Maria Carmela di Piazza","raw_affiliation_strings":["Consiglio Nazionale delle Ricerche (CNR), Palermo, Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Consiglio Nazionale delle Ricerche (CNR), Palermo, Italy","institution_ids":["https://openalex.org/I4210144062"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.2422,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.57578494,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"12","issue":null,"first_page":"3063","last_page":"3068"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11052","display_name":"Energy Load and Power Forecasting","score":0.9998999834060669,"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"}},"topics":[{"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/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/T10603","display_name":"Smart Grid Energy Management","score":0.9977999925613403,"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":0.7421746850013733},{"id":"https://openalex.org/keywords/python","display_name":"Python (programming language)","score":0.6648801565170288},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6479981541633606},{"id":"https://openalex.org/keywords/nonlinear-autoregressive-exogenous-model","display_name":"Nonlinear autoregressive exogenous model","score":0.5458271503448486},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4747988283634186},{"id":"https://openalex.org/keywords/autoregressive-model","display_name":"Autoregressive model","score":0.4618804156780243},{"id":"https://openalex.org/keywords/energy-management","display_name":"Energy management","score":0.45451849699020386},{"id":"https://openalex.org/keywords/electricity-generation","display_name":"Electricity generation","score":0.43053731322288513},{"id":"https://openalex.org/keywords/autoregressive-integrated-moving-average","display_name":"Autoregressive integrated moving average","score":0.41252440214157104},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3915838599205017},{"id":"https://openalex.org/keywords/energy","display_name":"Energy (signal processing)","score":0.3517501950263977},{"id":"https://openalex.org/keywords/power","display_name":"Power (physics)","score":0.31060558557510376},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.2202376127243042},{"id":"https://openalex.org/keywords/programming-language","display_name":"Programming language","score":0.08474558591842651}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7421746850013733},{"id":"https://openalex.org/C519991488","wikidata":"https://www.wikidata.org/wiki/Q28865","display_name":"Python (programming language)","level":2,"score":0.6648801565170288},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6479981541633606},{"id":"https://openalex.org/C42536954","wikidata":"https://www.wikidata.org/wiki/Q7049462","display_name":"Nonlinear autoregressive exogenous model","level":3,"score":0.5458271503448486},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4747988283634186},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.4618804156780243},{"id":"https://openalex.org/C7817414","wikidata":"https://www.wikidata.org/wiki/Q1779504","display_name":"Energy management","level":3,"score":0.45451849699020386},{"id":"https://openalex.org/C423512","wikidata":"https://www.wikidata.org/wiki/Q383973","display_name":"Electricity generation","level":3,"score":0.43053731322288513},{"id":"https://openalex.org/C24338571","wikidata":"https://www.wikidata.org/wiki/Q2566298","display_name":"Autoregressive integrated moving average","level":3,"score":0.41252440214157104},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3915838599205017},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.3517501950263977},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.31060558557510376},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.2202376127243042},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.08474558591842651},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"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/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iecon.2019.8926801","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iecon.2019.8926801","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society","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":0.8799999952316284}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":35,"referenced_works":["https://openalex.org/W52569599","https://openalex.org/W1980364886","https://openalex.org/W1992079190","https://openalex.org/W2012348248","https://openalex.org/W2031939255","https://openalex.org/W2049492347","https://openalex.org/W2055909153","https://openalex.org/W2056192250","https://openalex.org/W2058401212","https://openalex.org/W2060606400","https://openalex.org/W2067019903","https://openalex.org/W2068928057","https://openalex.org/W2080544073","https://openalex.org/W2129270932","https://openalex.org/W2145397365","https://openalex.org/W2146502635","https://openalex.org/W2157538353","https://openalex.org/W2164063344","https://openalex.org/W2171800554","https://openalex.org/W2179399249","https://openalex.org/W2207229987","https://openalex.org/W2268604234","https://openalex.org/W2523145457","https://openalex.org/W2550504915","https://openalex.org/W2558583184","https://openalex.org/W2562403923","https://openalex.org/W2571164490","https://openalex.org/W2896561370","https://openalex.org/W2946017343","https://openalex.org/W2953384591","https://openalex.org/W2963026732","https://openalex.org/W6665543067","https://openalex.org/W6681435938","https://openalex.org/W6683252029","https://openalex.org/W6713134421"],"related_works":["https://openalex.org/W3120843198","https://openalex.org/W4386110032","https://openalex.org/W2177401844","https://openalex.org/W2904357295","https://openalex.org/W2902707689","https://openalex.org/W4225851526","https://openalex.org/W2990899954","https://openalex.org/W4327796184","https://openalex.org/W2089574997","https://openalex.org/W2307785792"],"abstract_inverted_index":{"The":[0,53,77,143],"use":[1],"of":[2,14,25,29,49,55,61,133],"Energy":[3],"Management":[4],"Systems":[5],"(EMSs)":[6],"allows":[7],"obtaining":[8],"remarkable":[9],"advantages":[10],"for":[11,158],"both":[12],"end-users":[13],"electrical":[15],"energy":[16],"and":[17,32,106,120,138,155,161],"grid":[18],"operators.":[19],"These":[20],"systems":[21],"can":[22,66],"take":[23],"advantage":[24],"a":[26,45,62,73,82,115],"suitable":[27,83,157],"forecasting":[28,42,63,150],"load":[30],"demand":[31],"meteocli-matic":[33],"variables":[34],"tied":[35],"to":[36,69,71,126,164],"power":[37,51],"generation.":[38],"In":[39],"facts,":[40],"the":[41,50,59,89,148],"ability":[43],"enables":[44],"more":[46],"effective":[47],"planning":[48],"allocation.":[52],"aim":[54],"this":[56],"paper":[57],"is":[58,79,156],"development":[60],"module":[64,78,151],"that":[65,147],"be":[67],"interfaced":[68],"EMSs":[70],"deliver":[72],"24h":[74],"ahead":[75],"forecasting.":[76],"based":[80],"on":[81],"Artificial":[84],"Neural":[85],"Network":[86],"(ANN),":[87],"namely":[88],"nonlinear":[90],"autoregressive":[91],"with":[92,130],"exogenous":[93],"input":[94],"(NARX)":[95],"ANN.":[96],"Such":[97],"an":[98],"ANN":[99],"has":[100,111,152],"been":[101,112,124],"implemented":[102],"using":[103,114],"Tensorflow":[104],"library":[105],"writing":[107],"Python":[108],"code.":[109],"It":[110],"trained":[113],"public":[116],"solar":[117],"irradiance":[118],"dataset,":[119],"several":[121],"tests":[122],"have":[123],"performed":[125],"assess":[127],"its":[128],"performance":[129,154],"different":[131],"numbers":[132],"output":[134],"units,":[135],"hidden":[136,141],"layers,":[137],"neurons":[139],"per":[140],"layer.":[142],"obtained":[144,149],"results":[145],"show":[146],"good":[153],"embedded":[159],"implementation":[160],"online":[162],"operation":[163],"support":[165],"EMSs.":[166]},"counts_by_year":[{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
