{"id":"https://openalex.org/W2893798702","doi":"https://doi.org/10.1109/indin.2018.8471953","title":"Combining auto-regression with exogenous variables in sequence-to-sequence recurrent neural networks for short-term load forecasting","display_name":"Combining auto-regression with exogenous variables in sequence-to-sequence recurrent neural networks for short-term load forecasting","publication_year":2018,"publication_date":"2018-07-01","ids":{"openalex":"https://openalex.org/W2893798702","doi":"https://doi.org/10.1109/indin.2018.8471953","mag":"2893798702"},"language":"en","primary_location":{"id":"doi:10.1109/indin.2018.8471953","is_oa":false,"landing_page_url":"https://doi.org/10.1109/indin.2018.8471953","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE 16th International Conference on Industrial Informatics (INDIN)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://publications.rwth-aachen.de/search?p=id:%22RWTH-2018-231097%22","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5006325646","display_name":"Henning Wilms","orcid":null},"institutions":[{"id":"https://openalex.org/I887968799","display_name":"RWTH Aachen University","ror":"https://ror.org/04xfq0f34","country_code":"DE","type":"education","lineage":["https://openalex.org/I887968799"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Henning Wilms","raw_affiliation_strings":["Automation of Complex Power Systems, E.ON Energy Research Center, RWTH Aachen University, Aachen, Germany"],"affiliations":[{"raw_affiliation_string":"Automation of Complex Power Systems, E.ON Energy Research Center, RWTH Aachen University, Aachen, Germany","institution_ids":["https://openalex.org/I887968799"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028770313","display_name":"Marco Cupelli","orcid":"https://orcid.org/0000-0003-1570-5030"},"institutions":[{"id":"https://openalex.org/I887968799","display_name":"RWTH Aachen University","ror":"https://ror.org/04xfq0f34","country_code":"DE","type":"education","lineage":["https://openalex.org/I887968799"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Marco Cupelli","raw_affiliation_strings":["Automation of Complex Power Systems, E.ON Energy Research Center, RWTH Aachen University, Aachen, Germany"],"affiliations":[{"raw_affiliation_string":"Automation of Complex Power Systems, E.ON Energy Research Center, RWTH Aachen University, Aachen, Germany","institution_ids":["https://openalex.org/I887968799"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5030410007","display_name":"Antonello Monti","orcid":"https://orcid.org/0000-0003-1914-9801"},"institutions":[{"id":"https://openalex.org/I887968799","display_name":"RWTH Aachen University","ror":"https://ror.org/04xfq0f34","country_code":"DE","type":"education","lineage":["https://openalex.org/I887968799"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Antonello Monti","raw_affiliation_strings":["Automation of Complex Power Systems, E.ON Energy Research Center, RWTH Aachen University, Aachen, Germany"],"affiliations":[{"raw_affiliation_string":"Automation of Complex Power Systems, E.ON Energy Research Center, RWTH Aachen University, Aachen, Germany","institution_ids":["https://openalex.org/I887968799"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5006325646"],"corresponding_institution_ids":["https://openalex.org/I887968799"],"apc_list":null,"apc_paid":null,"fwci":0.785,"has_fulltext":false,"cited_by_count":17,"citation_normalized_percentile":{"value":0.74497852,"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":"673","last_page":"679"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11052","display_name":"Energy Load and Power Forecasting","score":1.0,"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":1.0,"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/T11326","display_name":"Stock Market Forecasting Methods","score":0.9973000288009644,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.996399998664856,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/sequence","display_name":"Sequence (biology)","score":0.6932789087295532},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6494698524475098},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6443092226982117},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5828537344932556},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.543973982334137},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.5363358855247498},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.5171524882316589},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.459117591381073},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.4589252769947052},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.45803192257881165},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.4335174560546875},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.43006274104118347},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.41400110721588135},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.380176305770874},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.1596459150314331},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.15294772386550903}],"concepts":[{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.6932789087295532},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6494698524475098},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6443092226982117},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5828537344932556},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.543973982334137},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.5363358855247498},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5171524882316589},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.459117591381073},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.4589252769947052},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.45803192257881165},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.4335174560546875},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.43006274104118347},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.41400110721588135},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.380176305770874},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.1596459150314331},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.15294772386550903},{"id":"https://openalex.org/C54355233","wikidata":"https://www.wikidata.org/wiki/Q7162","display_name":"Genetics","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"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/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/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/indin.2018.8471953","is_oa":false,"landing_page_url":"https://doi.org/10.1109/indin.2018.8471953","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE 16th International Conference on Industrial Informatics (INDIN)","raw_type":"proceedings-article"},{"id":"pmh:oai:publications.rwth-aachen.de:751103","is_oa":true,"landing_page_url":"https://publications.rwth-aachen.de/search?p=id:%22RWTH-2018-231097%22","pdf_url":null,"source":{"id":"https://openalex.org/S4306401033","display_name":"RWTH Publications (RWTH Aachen)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I887968799","host_organization_name":"RWTH Aachen University","host_organization_lineage":["https://openalex.org/I887968799"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Piscataway, NJ : IEEE, IEEE International Conference on Industrial Informatics INDIN 673-679 (2018). doi:10.1109/INDIN.2018.8471953","raw_type":"info:eu-repo/semantics/conferenceObject"}],"best_oa_location":{"id":"pmh:oai:publications.rwth-aachen.de:751103","is_oa":true,"landing_page_url":"https://publications.rwth-aachen.de/search?p=id:%22RWTH-2018-231097%22","pdf_url":null,"source":{"id":"https://openalex.org/S4306401033","display_name":"RWTH Publications (RWTH Aachen)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I887968799","host_organization_name":"RWTH Aachen University","host_organization_lineage":["https://openalex.org/I887968799"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Piscataway, NJ : IEEE, IEEE International Conference on Industrial Informatics INDIN 673-679 (2018). doi:10.1109/INDIN.2018.8471953","raw_type":"info:eu-repo/semantics/conferenceObject"},"sustainable_development_goals":[{"score":0.75,"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320320300","display_name":"European Commission","ror":"https://ror.org/00k4n6c32"},{"id":"https://openalex.org/F4320332999","display_name":"Horizon 2020 Framework Programme","ror":"https://ror.org/00k4n6c32"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W1598796236","https://openalex.org/W1815076433","https://openalex.org/W1924770834","https://openalex.org/W1989938710","https://openalex.org/W2016210396","https://openalex.org/W2064675550","https://openalex.org/W2083020303","https://openalex.org/W2095705004","https://openalex.org/W2107878631","https://openalex.org/W2116261113","https://openalex.org/W2130942839","https://openalex.org/W2147042673","https://openalex.org/W2157331557","https://openalex.org/W2275088575","https://openalex.org/W2296521892","https://openalex.org/W2489782956","https://openalex.org/W2599285715","https://openalex.org/W2612810742","https://openalex.org/W2613278213","https://openalex.org/W2754252319","https://openalex.org/W2766736793","https://openalex.org/W2884745705","https://openalex.org/W2950635152","https://openalex.org/W2998216295","https://openalex.org/W4285719527","https://openalex.org/W6635679246","https://openalex.org/W6640212811","https://openalex.org/W6674330103","https://openalex.org/W6743985089","https://openalex.org/W7066667914"],"related_works":["https://openalex.org/W2361261277","https://openalex.org/W4289356671","https://openalex.org/W2389155397","https://openalex.org/W2165884543","https://openalex.org/W2312753042","https://openalex.org/W1970763357","https://openalex.org/W3186837933","https://openalex.org/W2368989808","https://openalex.org/W2034959125","https://openalex.org/W2355687852"],"abstract_inverted_index":{"In":[0],"this":[1,41],"paper":[2],"we":[3],"propose":[4],"a":[5,24,82],"sequence-to-sequence":[6,45],"machine":[7,100],"learning":[8,101],"architecture":[9,19,46,80],"for":[10,32,63],"time-series":[11,107],"forecasting":[12,27,39,54,102],"based":[13],"on":[14,81],"recurrent":[15],"neural":[16],"networks.":[17],"This":[18],"can":[20],"be":[21],"used":[22],"as":[23,68,70,104,106],"general":[25],"purpose":[26],"method":[28],"and":[29],"is":[30,95,115],"evaluated":[31],"the":[33,78,88],"application":[34],"of":[35,52],"short-term":[36],"electric":[37],"load":[38,83],"in":[40],"paper.":[42],"The":[43,93,113],"proposed":[44,79],"<sup":[47,110],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[48,111],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">1</sup>":[49,112],"combines":[50],"elements":[51],"auto-regressive":[53],"techniques":[55,103],"with":[56],"multivariate":[57],"regression":[58],"by":[59,87],"including":[60],"exogenous":[61],"variables":[62],"each":[64],"forecasted":[65],"time":[66],"step":[67],"well":[69,105],"previous":[71],"values":[72],"when":[73],"inferring":[74],"forecasts.":[75],"We":[76],"assess":[77],"data":[84],"set":[85],"provided":[86],"Global":[89],"Energy":[90],"Forecasting":[91],"Competition.":[92],"conclusion":[94],"that":[96],"it":[97],"outperforms":[98],"other":[99],"analysis":[108],"methods.":[109],"implementation":[114],"available":[116],"at:":[117],"https://github.com/HenWil13/ieeeINDIN18":[118]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":1}],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2025-10-10T00:00:00"}