{"id":"https://openalex.org/W2907876867","doi":"https://doi.org/10.1109/icarcv.2018.8581235","title":"Day-Ahead Price Forecasting for Electricity Market using Long-Short Term Memory Recurrent Neural Network","display_name":"Day-Ahead Price Forecasting for Electricity Market using Long-Short Term Memory Recurrent Neural Network","publication_year":2018,"publication_date":"2018-11-01","ids":{"openalex":"https://openalex.org/W2907876867","doi":"https://doi.org/10.1109/icarcv.2018.8581235","mag":"2907876867"},"language":"en","primary_location":{"id":"doi:10.1109/icarcv.2018.8581235","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icarcv.2018.8581235","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV)","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/A5024713725","display_name":"LianLian Jiang","orcid":null},"institutions":[{"id":"https://openalex.org/I115228651","display_name":"Agency for Science, Technology and Research","ror":"https://ror.org/036wvzt09","country_code":"SG","type":"government","lineage":["https://openalex.org/I115228651"]},{"id":"https://openalex.org/I3005327000","display_name":"Institute for Infocomm Research","ror":"https://ror.org/053rfa017","country_code":"SG","type":"facility","lineage":["https://openalex.org/I115228651","https://openalex.org/I3005327000","https://openalex.org/I91275662"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"LianLian Jiang","raw_affiliation_strings":["A*STAR, Institute for Infocomm Research, Singapore"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"A*STAR, Institute for Infocomm Research, Singapore","institution_ids":["https://openalex.org/I3005327000","https://openalex.org/I115228651"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5050664552","display_name":"Guoqiang Hu","orcid":"https://orcid.org/0000-0002-8618-5581"},"institutions":[{"id":"https://openalex.org/I172675005","display_name":"Nanyang Technological University","ror":"https://ror.org/02e7b5302","country_code":"SG","type":"education","lineage":["https://openalex.org/I172675005"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Guoqiang Hu","raw_affiliation_strings":["School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore","institution_ids":["https://openalex.org/I172675005"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.6188,"has_fulltext":false,"cited_by_count":51,"citation_normalized_percentile":{"value":0.90697807,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"949","last_page":"954"},"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/T10424","display_name":"Electric Power System Optimization","score":0.9970999956130981,"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.9793000221252441,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/mean-absolute-percentage-error","display_name":"Mean absolute percentage error","score":0.7795674204826355},{"id":"https://openalex.org/keywords/electricity-price-forecasting","display_name":"Electricity price forecasting","score":0.7119098901748657},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6587222218513489},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.6562924385070801},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6188002228736877},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.5486732721328735},{"id":"https://openalex.org/keywords/electricity-market","display_name":"Electricity market","score":0.5466093420982361},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.5363159775733948},{"id":"https://openalex.org/keywords/electricity","display_name":"Electricity","score":0.5216015577316284},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.49255093932151794},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4474645256996155},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.37331920862197876},{"id":"https://openalex.org/keywords/economics","display_name":"Economics","score":0.2648013234138489},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.13401582837104797}],"concepts":[{"id":"https://openalex.org/C150217764","wikidata":"https://www.wikidata.org/wiki/Q6803607","display_name":"Mean absolute percentage error","level":3,"score":0.7795674204826355},{"id":"https://openalex.org/C2781104810","wikidata":"https://www.wikidata.org/wiki/Q23580049","display_name":"Electricity price forecasting","level":4,"score":0.7119098901748657},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6587222218513489},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.6562924385070801},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6188002228736877},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.5486732721328735},{"id":"https://openalex.org/C146733006","wikidata":"https://www.wikidata.org/wiki/Q676081","display_name":"Electricity market","level":3,"score":0.5466093420982361},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5363159775733948},{"id":"https://openalex.org/C206658404","wikidata":"https://www.wikidata.org/wiki/Q12725","display_name":"Electricity","level":2,"score":0.5216015577316284},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.49255093932151794},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4474645256996155},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.37331920862197876},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.2648013234138489},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.13401582837104797},{"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/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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icarcv.2018.8581235","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icarcv.2018.8581235","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":35,"referenced_works":["https://openalex.org/W1982870152","https://openalex.org/W1993601879","https://openalex.org/W2002044606","https://openalex.org/W2013872183","https://openalex.org/W2040198354","https://openalex.org/W2048273813","https://openalex.org/W2064675550","https://openalex.org/W2096220984","https://openalex.org/W2103187549","https://openalex.org/W2113244375","https://openalex.org/W2113427226","https://openalex.org/W2125747076","https://openalex.org/W2126709108","https://openalex.org/W2132591205","https://openalex.org/W2137699278","https://openalex.org/W2141484579","https://openalex.org/W2144438856","https://openalex.org/W2151310832","https://openalex.org/W2155482907","https://openalex.org/W2156341689","https://openalex.org/W2158984605","https://openalex.org/W2168185444","https://openalex.org/W2168811909","https://openalex.org/W2171985965","https://openalex.org/W2286305802","https://openalex.org/W2607851870","https://openalex.org/W2748523110","https://openalex.org/W2799827709","https://openalex.org/W2907780353","https://openalex.org/W2964121744","https://openalex.org/W3122855307","https://openalex.org/W4233360132","https://openalex.org/W4243351757","https://openalex.org/W6680421994","https://openalex.org/W6758068450"],"related_works":["https://openalex.org/W2719315100","https://openalex.org/W2139581356","https://openalex.org/W3208495680","https://openalex.org/W2347295811","https://openalex.org/W2911667360","https://openalex.org/W2028449551","https://openalex.org/W2088353375","https://openalex.org/W2162537764","https://openalex.org/W2994323835","https://openalex.org/W1996619742"],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"an":[3],"efficient":[4],"method":[5,168],"for":[6,62,77,128,153,201],"the":[7,55,59,73,96,108,112,125,129,166,182,197,202],"day-ahead":[8,74],"electricity":[9,75,126],"price":[10,76,127],"forecasting":[11,188],"(EPF)":[12],"is":[13,45,124,169],"proposed":[14,167],"based":[15],"on":[16,58],"a":[17,141],"long-short":[18],"term":[19,52],"memory":[20],"(LSTM)":[21],"recurrent":[22],"neural":[23],"network":[24,27,184],"model.":[25],"LSTM":[26,69,183],"has":[28],"been":[29],"widely":[30],"used":[31],"in":[32,140,156,196],"various":[33],"applications":[34],"such":[35,103],"as":[36,93,104],"natural":[37],"language":[38],"processing":[39],"and":[40,50,84,118,158,190],"time":[41],"series":[42],"analysis.":[43],"It":[44],"capable":[46],"of":[47,54,88,107,111,136,150,165],"learning":[48],"features":[49],"long":[51],"dependencies":[53],"historical":[56,91,119],"information":[57],"current":[60],"predictions":[61],"sequential":[63],"data.":[64],"We":[65],"propose":[66],"to":[67,71,95,193],"use":[68],"model":[70],"forecast":[72],"Australian":[78],"market":[79,173],"at":[80],"Victoria":[81],"(VIC)":[82],"region":[83],"Singapore":[85,159],"market.":[86,204],"Instead":[87],"using":[89,171],"only":[90],"prices":[92,117,137],"inputs":[94],"model,":[97],"we":[98],"also":[99],"consider":[100],"exogenous":[101],"variables,":[102],"holidays,":[105],"day":[106],"week,":[109],"hour":[110],"day,":[113],"weather":[114],"conditions,":[115],"oil":[116],"price/demand,":[120],"etc.":[121],"The":[122,132,144,163,178],"output":[123],"next":[130],"hour.":[131],"future":[133],"24":[134],"hours":[135],"are":[138,161],"forecasted":[139],"recursive":[142],"manner.":[143],"mean":[145],"absolute":[146],"percentage":[147],"error":[148],"(MAPE)":[149],"four":[151,186],"weeks":[152],"each":[154],"season":[155],"VIC":[157,203],"markets":[160],"examined.":[162],"effectiveness":[164],"verified":[170],"real":[172],"data":[174],"from":[175],"both":[176],"markets.":[177],"result":[179],"shows":[180],"that":[181],"outperforms":[185],"popular":[187],"methods":[189],"provides":[191],"up":[192],"47.3%":[194],"improvement":[195],"average":[198],"daily":[199],"MAPE":[200]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":15},{"year":2022,"cited_by_count":7},{"year":2021,"cited_by_count":6},{"year":2020,"cited_by_count":10},{"year":2019,"cited_by_count":3},{"year":2018,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
