{"id":"https://openalex.org/W7083313728","doi":"https://doi.org/10.48550/arxiv.2509.19374","title":"Short-Term Regional Electricity Demand Forecasting in Argentina Using LSTM Networks","display_name":"Short-Term Regional Electricity Demand Forecasting in Argentina Using LSTM Networks","publication_year":2025,"publication_date":"2025-09-19","ids":{"openalex":"https://openalex.org/W7083313728","doi":"https://doi.org/10.48550/arxiv.2509.19374"},"language":"en","primary_location":{"id":"doi:10.48550/arxiv.2509.19374","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2509.19374","pdf_url":null,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2509.19374","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Oviedo, Oscar A.","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Oviedo, Oscar A.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":true,"primary_topic":{"id":"https://openalex.org/T12642","display_name":"Polymer-Based Agricultural Enhancements","score":0.042899999767541885,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical 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/T12642","display_name":"Polymer-Based Agricultural Enhancements","score":0.042899999767541885,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical 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/T14323","display_name":"Agriculture, Water, and Health","score":0.04050000011920929,"subfield":{"id":"https://openalex.org/subfields/2312","display_name":"Water Science and Technology"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11311","display_name":"Soil and Water Nutrient Dynamics","score":0.02539999969303608,"subfield":{"id":"https://openalex.org/subfields/2304","display_name":"Environmental Chemistry"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/interpretability","display_name":"Interpretability","score":0.6334999799728394},{"id":"https://openalex.org/keywords/generalizability-theory","display_name":"Generalizability theory","score":0.5737000107765198},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.5715000033378601},{"id":"https://openalex.org/keywords/hyperparameter","display_name":"Hyperparameter","score":0.5329999923706055},{"id":"https://openalex.org/keywords/demand-forecasting","display_name":"Demand forecasting","score":0.49070000648498535},{"id":"https://openalex.org/keywords/electricity-demand","display_name":"Electricity demand","score":0.48739999532699585},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.46720001101493835},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.4544000029563904},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.42160001397132874}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.6334999799728394},{"id":"https://openalex.org/C27158222","wikidata":"https://www.wikidata.org/wiki/Q5532422","display_name":"Generalizability theory","level":2,"score":0.5737000107765198},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5715000033378601},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5715000033378601},{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.5329999923706055},{"id":"https://openalex.org/C193809577","wikidata":"https://www.wikidata.org/wiki/Q3409300","display_name":"Demand forecasting","level":2,"score":0.49070000648498535},{"id":"https://openalex.org/C2988649059","wikidata":"https://www.wikidata.org/wiki/Q1853339","display_name":"Electricity demand","level":4,"score":0.48739999532699585},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.46720001101493835},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.4544000029563904},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.43470001220703125},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.42160001397132874},{"id":"https://openalex.org/C10485038","wikidata":"https://www.wikidata.org/wiki/Q48996162","display_name":"Hyperparameter optimization","level":3,"score":0.3928000032901764},{"id":"https://openalex.org/C206658404","wikidata":"https://www.wikidata.org/wiki/Q12725","display_name":"Electricity","level":2,"score":0.37549999356269836},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3725999891757965},{"id":"https://openalex.org/C2780150128","wikidata":"https://www.wikidata.org/wiki/Q21948731","display_name":"Extreme learning machine","level":3,"score":0.3682999908924103},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.3610000014305115},{"id":"https://openalex.org/C10558101","wikidata":"https://www.wikidata.org/wiki/Q689855","display_name":"Smart grid","level":2,"score":0.35339999198913574},{"id":"https://openalex.org/C30772137","wikidata":"https://www.wikidata.org/wiki/Q5164762","display_name":"Consumption (sociology)","level":2,"score":0.35280001163482666},{"id":"https://openalex.org/C158154518","wikidata":"https://www.wikidata.org/wiki/Q7310970","display_name":"Relevance (law)","level":2,"score":0.3456999957561493},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3366999924182892},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.31769999861717224},{"id":"https://openalex.org/C44882253","wikidata":"https://www.wikidata.org/wiki/Q3455882","display_name":"Multivariate adaptive regression splines","level":4,"score":0.304500013589859},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.29580000042915344},{"id":"https://openalex.org/C182365436","wikidata":"https://www.wikidata.org/wiki/Q50701","display_name":"Variable (mathematics)","level":2,"score":0.28700000047683716},{"id":"https://openalex.org/C150217764","wikidata":"https://www.wikidata.org/wiki/Q6803607","display_name":"Mean absolute percentage error","level":3,"score":0.2858000099658966},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.28040000796318054},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2773999869823456},{"id":"https://openalex.org/C2779438525","wikidata":"https://www.wikidata.org/wiki/Q5255048","display_name":"Demand response","level":3,"score":0.2768999934196472},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.2728999853134155},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2696000039577484},{"id":"https://openalex.org/C85617194","wikidata":"https://www.wikidata.org/wiki/Q2072794","display_name":"Particle swarm optimization","level":2,"score":0.26019999384880066},{"id":"https://openalex.org/C122282355","wikidata":"https://www.wikidata.org/wiki/Q7246855","display_name":"Probabilistic forecasting","level":3,"score":0.2551000118255615}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2509.19374","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2509.19374","pdf_url":null,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2509.19374","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2509.19374","pdf_url":null,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"This":[0],"study":[1,110],"presents":[2],"the":[3,42,82,87,92,98,117,132,148,165,172],"development":[4],"and":[5,39,56,68,77,84,94,123,138,151,168,184],"optimization":[6,96],"of":[7,54,60,64,86,97,120,127,134,147,158,171],"a":[8,49,57],"deep":[9],"learning":[10],"model":[11,43,128],"based":[12],"on":[13],"Long":[14],"Short-Term":[15],"Memory":[16],"(LSTM)":[17],"networks":[18],"to":[19,73,91,115],"predict":[20],"short-term":[21],"hourly":[22],"electricity":[23],"demand":[24,136,189],"in":[25,130,143,155],"C\u00f3rdoba,":[26],"Argentina.":[27],"Integrating":[28],"historical":[29],"consumption":[30,79],"data":[31],"with":[32,48],"exogenous":[33,121],"variables":[34,70],"(climatic":[35],"factors,":[36],"temporal":[37,66],"cycles,":[38],"demographic":[40],"statistics),":[41],"achieved":[44],"high":[45],"predictive":[46,166],"precision,":[47],"mean":[50],"absolute":[51],"percentage":[52],"error":[53],"3.20\\%":[55],"determination":[58],"coefficient":[59],"0.95.":[61],"The":[62],"inclusion":[63],"periodic":[65],"encodings":[67],"weather":[69],"proved":[71],"crucial":[72],"capture":[74],"seasonal":[75],"patterns":[76],"extreme":[78],"events,":[80],"enhancing":[81],"robustness":[83],"generalizability":[85],"model.":[88],"In":[89],"addition":[90],"design":[93],"hyperparameter":[95],"LSTM":[99],"architecture,":[100],"two":[101],"complementary":[102],"analyses":[103],"were":[104],"carried":[105],"out:":[106],"(i)":[107],"an":[108,125],"interpretability":[109],"using":[111],"Random":[112],"Forest":[113],"regression":[114],"quantify":[116],"relative":[118],"importance":[119],"drivers,":[122],"(ii)":[124],"evaluation":[126],"performance":[129],"predicting":[131],"timing":[133],"daily":[135],"maxima":[137],"minima,":[139],"achieving":[140],"exact-hour":[141],"accuracy":[142,167],"more":[144],"than":[145],"two-thirds":[146],"test":[149],"days":[150],"within":[152],"abs(1)":[153],"hour":[154],"over":[156],"90\\%":[157],"cases.":[159],"Together,":[160],"these":[161],"results":[162],"highlight":[163],"both":[164],"operational":[169],"relevance":[170],"proposed":[173],"framework,":[174],"providing":[175],"valuable":[176],"insights":[177],"for":[178],"grid":[179],"operators":[180],"seeking":[181],"optimized":[182],"planning":[183],"control":[185],"strategies":[186],"under":[187],"diverse":[188],"scenarios.":[190]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2025-10-10T00:00:00"}
