{"id":"https://openalex.org/W2613278213","doi":"https://doi.org/10.1109/ciss.2017.7926112","title":"Electric load forecasting in smart grids using Long-Short-Term-Memory based Recurrent Neural Network","display_name":"Electric load forecasting in smart grids using Long-Short-Term-Memory based Recurrent Neural Network","publication_year":2017,"publication_date":"2017-03-01","ids":{"openalex":"https://openalex.org/W2613278213","doi":"https://doi.org/10.1109/ciss.2017.7926112","mag":"2613278213"},"language":"en","primary_location":{"id":"doi:10.1109/ciss.2017.7926112","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ciss.2017.7926112","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 51st Annual Conference on Information Sciences and Systems (CISS)","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/A5062254830","display_name":"Jian Zheng","orcid":"https://orcid.org/0000-0002-2740-9039"},"institutions":[{"id":"https://openalex.org/I123946342","display_name":"Binghamton University","ror":"https://ror.org/008rmbt77","country_code":"US","type":"education","lineage":["https://openalex.org/I123946342"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jian Zheng","raw_affiliation_strings":["Department of Electrical and Computer Engineering, State University of New York at Binghamton, Binghamton, NY"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, State University of New York at Binghamton, Binghamton, NY","institution_ids":["https://openalex.org/I123946342"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100996622","display_name":"Cencen Xu","orcid":null},"institutions":[{"id":"https://openalex.org/I123946342","display_name":"Binghamton University","ror":"https://ror.org/008rmbt77","country_code":"US","type":"education","lineage":["https://openalex.org/I123946342"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Cencen Xu","raw_affiliation_strings":["Department of Electrical and Computer Engineering, State University of New York at Binghamton, Binghamton, NY"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, State University of New York at Binghamton, Binghamton, NY","institution_ids":["https://openalex.org/I123946342"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020551506","display_name":"Ziang Zhang","orcid":"https://orcid.org/0000-0003-1240-5710"},"institutions":[{"id":"https://openalex.org/I123946342","display_name":"Binghamton University","ror":"https://ror.org/008rmbt77","country_code":"US","type":"education","lineage":["https://openalex.org/I123946342"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ziang Zhang","raw_affiliation_strings":["Department of Electrical and Computer Engineering, State University of New York at Binghamton, Binghamton, NY"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, State University of New York at Binghamton, Binghamton, NY","institution_ids":["https://openalex.org/I123946342"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100434106","display_name":"Xiaohua Li","orcid":"https://orcid.org/0000-0002-3604-8634"},"institutions":[{"id":"https://openalex.org/I123946342","display_name":"Binghamton University","ror":"https://ror.org/008rmbt77","country_code":"US","type":"education","lineage":["https://openalex.org/I123946342"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xiaohua Li","raw_affiliation_strings":["Department of Electrical and Computer Engineering, State University of New York at Binghamton, Binghamton, NY"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, State University of New York at Binghamton, Binghamton, NY","institution_ids":["https://openalex.org/I123946342"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I123946342"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":344,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"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/T11326","display_name":"Stock Market Forecasting Methods","score":0.998199999332428,"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":0.9929999709129333,"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/recurrent-neural-network","display_name":"Recurrent neural network","score":0.7870262861251831},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7396242618560791},{"id":"https://openalex.org/keywords/electrical-load","display_name":"Electrical load","score":0.7107136249542236},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.6408189535140991},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6057828664779663},{"id":"https://openalex.org/keywords/smart-grid","display_name":"Smart grid","score":0.5832826495170593},{"id":"https://openalex.org/keywords/electric-power-system","display_name":"Electric power system","score":0.5654911398887634},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.4627307653427124},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.45479467511177063},{"id":"https://openalex.org/keywords/nonlinear-system","display_name":"Nonlinear system","score":0.41309282183647156},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.38809022307395935},{"id":"https://openalex.org/keywords/power","display_name":"Power (physics)","score":0.30069345235824585},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.2958412766456604},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.1407080590724945},{"id":"https://openalex.org/keywords/electrical-engineering","display_name":"Electrical engineering","score":0.06841966509819031}],"concepts":[{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.7870262861251831},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7396242618560791},{"id":"https://openalex.org/C77715397","wikidata":"https://www.wikidata.org/wiki/Q931447","display_name":"Electrical load","level":3,"score":0.7107136249542236},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.6408189535140991},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6057828664779663},{"id":"https://openalex.org/C10558101","wikidata":"https://www.wikidata.org/wiki/Q689855","display_name":"Smart grid","level":2,"score":0.5832826495170593},{"id":"https://openalex.org/C89227174","wikidata":"https://www.wikidata.org/wiki/Q2388981","display_name":"Electric power system","level":3,"score":0.5654911398887634},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.4627307653427124},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.45479467511177063},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.41309282183647156},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.38809022307395935},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.30069345235824585},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2958412766456604},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.1407080590724945},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.06841966509819031},{"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/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","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/ciss.2017.7926112","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ciss.2017.7926112","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 51st Annual Conference on Information Sciences and Systems (CISS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6200000047683716,"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":39,"referenced_works":["https://openalex.org/W105632781","https://openalex.org/W777046221","https://openalex.org/W1598796236","https://openalex.org/W1815076433","https://openalex.org/W1984755515","https://openalex.org/W2001506129","https://openalex.org/W2003706483","https://openalex.org/W2005708641","https://openalex.org/W2014928429","https://openalex.org/W2015427029","https://openalex.org/W2024493895","https://openalex.org/W2029784539","https://openalex.org/W2039133703","https://openalex.org/W2059804518","https://openalex.org/W2060217768","https://openalex.org/W2064675550","https://openalex.org/W2073002835","https://openalex.org/W2074121977","https://openalex.org/W2083477834","https://openalex.org/W2095551575","https://openalex.org/W2096501632","https://openalex.org/W2107878631","https://openalex.org/W2117014758","https://openalex.org/W2130942839","https://openalex.org/W2145856394","https://openalex.org/W2152098675","https://openalex.org/W2293634267","https://openalex.org/W2294097774","https://openalex.org/W2462821756","https://openalex.org/W2519138593","https://openalex.org/W2606560580","https://openalex.org/W3082644425","https://openalex.org/W4249179470","https://openalex.org/W4285719527","https://openalex.org/W6622444566","https://openalex.org/W6638545294","https://openalex.org/W6679436768","https://openalex.org/W6718669786","https://openalex.org/W6726391044"],"related_works":["https://openalex.org/W4225394202","https://openalex.org/W4298287631","https://openalex.org/W2953061907","https://openalex.org/W1847088711","https://openalex.org/W3036642985","https://openalex.org/W3032952384","https://openalex.org/W3017902212","https://openalex.org/W2093411737","https://openalex.org/W3158513142","https://openalex.org/W2041760049"],"abstract_inverted_index":{"Electric":[0],"load":[1,13,17,39,79,102],"forecasting":[2,14,48,97,108,117],"plays":[3],"a":[4,106],"vital":[5],"role":[6],"in":[7,42,76],"smart":[8],"grids.":[9],"Short":[10],"term":[11,37,74],"electric":[12,38,78,101],"forecasts":[15],"the":[16,28,35,72,77,99],"that":[18,91],"is":[19,49,68,94],"several":[20,23],"hours":[21],"to":[22,27,61,70,89,114],"weeks":[24],"ahead.":[25],"Due":[26],"nonlinear,":[29],"non-stationary":[30],"and":[31],"nonseasonal":[32],"nature":[33],"of":[34,96],"short":[36],"time":[40,80,103],"series":[41,81,104],"small":[43],"scale":[44],"power":[45],"systems,":[46],"accurate":[47,84],"challenging.":[50],"This":[51],"paper":[52],"explores":[53],"Long-Short-Term-Memory":[54],"(LSTM)":[55],"based":[56],"Recurrent":[57],"Neural":[58],"Network":[59],"(RNN)":[60],"deal":[62],"with":[63,105],"this":[64],"challenge.":[65],"LSTM-based":[66,92],"RNN":[67,93],"able":[69],"exploit":[71],"long":[73,107],"dependencies":[75],"for":[82],"more":[83],"forecasting.":[85],"Experiments":[86],"are":[87],"conducted":[88],"demonstrate":[90],"capable":[95],"accurately":[98],"complex":[100],"horizon.":[109],"Its":[110],"performance":[111],"compares":[112],"favorably":[113],"many":[115],"other":[116],"methods.":[118]},"counts_by_year":[{"year":2026,"cited_by_count":7},{"year":2025,"cited_by_count":26},{"year":2024,"cited_by_count":37},{"year":2023,"cited_by_count":54},{"year":2022,"cited_by_count":50},{"year":2021,"cited_by_count":46},{"year":2020,"cited_by_count":60},{"year":2019,"cited_by_count":33},{"year":2018,"cited_by_count":28},{"year":2017,"cited_by_count":3}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}