{"id":"https://openalex.org/W2982966244","doi":"https://doi.org/10.1109/rtsi.2019.8895580","title":"Weather Based Day-Ahead and Week-Ahead Load Forecasting using Deep Recurrent Neural Network","display_name":"Weather Based Day-Ahead and Week-Ahead Load Forecasting using Deep Recurrent Neural Network","publication_year":2019,"publication_date":"2019-09-01","ids":{"openalex":"https://openalex.org/W2982966244","doi":"https://doi.org/10.1109/rtsi.2019.8895580","mag":"2982966244"},"language":"en","primary_location":{"id":"doi:10.1109/rtsi.2019.8895580","is_oa":false,"landing_page_url":"https://doi.org/10.1109/rtsi.2019.8895580","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE 5th International forum on Research and Technology for Society and Industry (RTSI)","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":null,"display_name":"Mingzhe Zou","orcid":null},"institutions":[{"id":"https://openalex.org/I98677209","display_name":"University of Edinburgh","ror":"https://ror.org/01nrxwf90","country_code":"GB","type":"education","lineage":["https://openalex.org/I98677209"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Mingzhe Zou","raw_affiliation_strings":["School of Engineering, The University of Edinburgh, Edinburgh, UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Engineering, The University of Edinburgh, Edinburgh, UK","institution_ids":["https://openalex.org/I98677209"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110573847","display_name":"Duo Fang","orcid":"https://orcid.org/0009-0004-2692-5199"},"institutions":[{"id":"https://openalex.org/I98677209","display_name":"University of Edinburgh","ror":"https://ror.org/01nrxwf90","country_code":"GB","type":"education","lineage":["https://openalex.org/I98677209"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Duo Fang","raw_affiliation_strings":["School of Engineering, The University of Edinburgh, Edinburgh, UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Engineering, The University of Edinburgh, Edinburgh, UK","institution_ids":["https://openalex.org/I98677209"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027402617","display_name":"Gareth Harrison","orcid":"https://orcid.org/0000-0003-1697-630X"},"institutions":[{"id":"https://openalex.org/I98677209","display_name":"University of Edinburgh","ror":"https://ror.org/01nrxwf90","country_code":"GB","type":"education","lineage":["https://openalex.org/I98677209"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Gareth Harrison","raw_affiliation_strings":["School of Engineering, The University of Edinburgh, Edinburgh, UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Engineering, The University of Edinburgh, Edinburgh, UK","institution_ids":["https://openalex.org/I98677209"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5057974305","display_name":"Sasa Djokic","orcid":"https://orcid.org/0000-0003-1980-1329"},"institutions":[{"id":"https://openalex.org/I98677209","display_name":"University of Edinburgh","ror":"https://ror.org/01nrxwf90","country_code":"GB","type":"education","lineage":["https://openalex.org/I98677209"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Sasa Djokic","raw_affiliation_strings":["School of Engineering, The University of Edinburgh, Edinburgh, UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Engineering, The University of Edinburgh, Edinburgh, UK","institution_ids":["https://openalex.org/I98677209"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I98677209"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":26,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"341","last_page":"346"},"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.9882000088691711,"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"}},{"id":"https://openalex.org/T11276","display_name":"Solar Radiation and Photovoltaics","score":0.9437999725341797,"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/computer-science","display_name":"Computer science","score":0.6568734645843506},{"id":"https://openalex.org/keywords/schedule","display_name":"Schedule","score":0.628259539604187},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.616896390914917},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5978683829307556},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.5763845443725586},{"id":"https://openalex.org/keywords/demand-forecasting","display_name":"Demand forecasting","score":0.514153778553009},{"id":"https://openalex.org/keywords/probabilistic-forecasting","display_name":"Probabilistic forecasting","score":0.46348124742507935},{"id":"https://openalex.org/keywords/weather-forecasting","display_name":"Weather forecasting","score":0.4538690745830536},{"id":"https://openalex.org/keywords/electricity","display_name":"Electricity","score":0.4408063292503357},{"id":"https://openalex.org/keywords/power-demand","display_name":"Power demand","score":0.4255180358886719},{"id":"https://openalex.org/keywords/meteorology","display_name":"Meteorology","score":0.40925538539886475},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.39132508635520935},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3708250820636749},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.3689571022987366},{"id":"https://openalex.org/keywords/operations-research","display_name":"Operations research","score":0.3269869089126587},{"id":"https://openalex.org/keywords/power","display_name":"Power (physics)","score":0.29001379013061523},{"id":"https://openalex.org/keywords/power-consumption","display_name":"Power consumption","score":0.20348039269447327},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.16376855969429016},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.11693811416625977}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6568734645843506},{"id":"https://openalex.org/C68387754","wikidata":"https://www.wikidata.org/wiki/Q7271585","display_name":"Schedule","level":2,"score":0.628259539604187},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.616896390914917},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5978683829307556},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5763845443725586},{"id":"https://openalex.org/C193809577","wikidata":"https://www.wikidata.org/wiki/Q3409300","display_name":"Demand forecasting","level":2,"score":0.514153778553009},{"id":"https://openalex.org/C122282355","wikidata":"https://www.wikidata.org/wiki/Q7246855","display_name":"Probabilistic forecasting","level":3,"score":0.46348124742507935},{"id":"https://openalex.org/C21001229","wikidata":"https://www.wikidata.org/wiki/Q182868","display_name":"Weather forecasting","level":2,"score":0.4538690745830536},{"id":"https://openalex.org/C206658404","wikidata":"https://www.wikidata.org/wiki/Q12725","display_name":"Electricity","level":2,"score":0.4408063292503357},{"id":"https://openalex.org/C2983317576","wikidata":"https://www.wikidata.org/wiki/Q1853339","display_name":"Power demand","level":4,"score":0.4255180358886719},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.40925538539886475},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39132508635520935},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3708250820636749},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.3689571022987366},{"id":"https://openalex.org/C42475967","wikidata":"https://www.wikidata.org/wiki/Q194292","display_name":"Operations research","level":1,"score":0.3269869089126587},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.29001379013061523},{"id":"https://openalex.org/C2984118289","wikidata":"https://www.wikidata.org/wiki/Q29954","display_name":"Power consumption","level":3,"score":0.20348039269447327},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.16376855969429016},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.11693811416625977},{"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/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","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/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/rtsi.2019.8895580","is_oa":false,"landing_page_url":"https://doi.org/10.1109/rtsi.2019.8895580","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE 5th International forum on Research and Technology for Society and Industry (RTSI)","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":29,"referenced_works":["https://openalex.org/W185455334","https://openalex.org/W193089420","https://openalex.org/W1522301498","https://openalex.org/W1527772862","https://openalex.org/W1669302834","https://openalex.org/W1904365287","https://openalex.org/W2003223511","https://openalex.org/W2004915807","https://openalex.org/W2017870209","https://openalex.org/W2042051376","https://openalex.org/W2064675550","https://openalex.org/W2083172453","https://openalex.org/W2097781780","https://openalex.org/W2116048577","https://openalex.org/W2131774270","https://openalex.org/W2183936775","https://openalex.org/W2185592418","https://openalex.org/W2519385971","https://openalex.org/W2530867817","https://openalex.org/W2554861503","https://openalex.org/W2754252319","https://openalex.org/W2764791077","https://openalex.org/W2789822877","https://openalex.org/W2811152378","https://openalex.org/W2964121744","https://openalex.org/W6631190155","https://openalex.org/W6631439307","https://openalex.org/W6654883909","https://openalex.org/W6730045385"],"related_works":["https://openalex.org/W4391233944","https://openalex.org/W4362706849","https://openalex.org/W4310372331","https://openalex.org/W245708997","https://openalex.org/W1529580051","https://openalex.org/W2016363698","https://openalex.org/W1969424709","https://openalex.org/W1585017814","https://openalex.org/W2036352780","https://openalex.org/W2385950731"],"abstract_inverted_index":{"The":[0,161,188],"main":[1],"purpose":[2],"of":[3,12,27,42,46,52,85,95,99,103,154,169],"load":[4,60,131,181,208],"forecasting":[5,132],"is":[6,18,147,163],"to":[7,65,194],"provide":[8,201],"an":[9],"accurate":[10],"estimation":[11],"the":[13,23,96,100,104],"future":[14,32],"electricity":[15,29],"demands,":[16],"which":[17,146,175],"becoming":[19],"increasingly":[20],"important":[21],"for":[22,151,174,203],"operation":[24],"and":[25,31,49,56,72,91,182,191,206],"planning":[26],"existing":[28],"network":[30,143],"smart":[33],"grids,":[34],"as":[35,114,116],"they":[36],"will":[37],"feature":[38],"much":[39],"higher":[40],"ranges":[41],"uncertainties,":[43],"larger":[44],"variations":[45,84],"power":[47],"flows":[48],"increased":[50],"levels":[51],"interactions":[53],"between":[54],"supply":[55],"demand":[57,69,87],"sides.":[58],"Typical":[59],"profiles":[61],"exhibit":[62],"periodicity,":[63],"allowing":[64],"extract":[66],"patterns":[67],"from":[68],"time":[70],"series":[71],"available":[73],"historical":[74],"recordings.":[75],"However,":[76],"there":[77],"are":[78,186],"many":[79],"factors":[80,121],"that":[81],"cause":[82],"strong":[83],"these":[86],"patterns,":[88],"including":[89],"calendar":[90],"socio-behavioral":[92],"aspects":[93],"(time":[94],"day,":[97],"day":[98],"week,":[101],"season":[102],"year,":[105],"but":[106],"also":[107],"weekly":[108],"working":[109],"schedule,":[110],"public":[111],"holidays,":[112],"etc.),":[113],"well":[115],"meteorological":[117,213],"or":[118],"weather":[119,183],"related":[120],"(ambient":[122],"temperature,":[123],"solar":[124],"irradiation,":[125],"precipitation,":[126],"etc.).":[127],"This":[128],"paper":[129],"analyzes":[130],"using":[133],"a":[134,148,166,176],"stacked":[135],"bidirectional":[136],"long":[137],"short-term":[138],"memory":[139],"(SB-LSTM)":[140],"recurrent":[141],"neural":[142],"based":[144],"approach,":[145],"state-of-the-art":[149],"method":[150],"regression":[152],"analysis":[153,162,192],"time-series":[155],"data":[156,184],"under":[157],"deep":[158],"learning":[159],"framework.":[160],"performed":[164],"on":[165],"case":[167],"study":[168],"residential":[170],"demands":[171],"in":[172],"Scotland,":[173],"five-year-Iength":[177],"datasets":[178],"containing":[179],"both":[180,204],"recordings":[185],"available.":[187],"presented":[189],"results":[190],"allow":[193],"evaluate":[195],"how":[196],"accurately":[197],"SB-LSTM":[198],"approach":[199],"can":[200],"predictions":[202],"day-ahead":[205],"week-ahead":[207],"forecasting,":[209],"taking":[210],"into":[211],"account":[212],"information.":[214]},"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":8},{"year":2022,"cited_by_count":8},{"year":2021,"cited_by_count":5},{"year":2020,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}