{"id":"https://openalex.org/W4408258665","doi":"https://doi.org/10.1109/tencon61640.2024.10903110","title":"A Novel Machine Learning Approach to Optimize Energy Forecasting in Industry","display_name":"A Novel Machine Learning Approach to Optimize Energy Forecasting in Industry","publication_year":2024,"publication_date":"2024-12-01","ids":{"openalex":"https://openalex.org/W4408258665","doi":"https://doi.org/10.1109/tencon61640.2024.10903110"},"language":"en","primary_location":{"id":"doi:10.1109/tencon61640.2024.10903110","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tencon61640.2024.10903110","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"TENCON 2024 - 2024 IEEE Region 10 Conference (TENCON)","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":"Venkat Subramanian Arumuga Perumal","orcid":null},"institutions":[{"id":"https://openalex.org/I8696757","display_name":"Singapore University of Social Sciences","ror":"https://ror.org/01s57k749","country_code":"SG","type":"education","lineage":["https://openalex.org/I8696757"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Venkat Subramanian Arumuga Perumal","raw_affiliation_strings":["School of Science and Technology, Singapore University of Social Sciences,Singapore"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Science and Technology, Singapore University of Social Sciences,Singapore","institution_ids":["https://openalex.org/I8696757"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5077528029","display_name":"June Tay","orcid":null},"institutions":[{"id":"https://openalex.org/I8696757","display_name":"Singapore University of Social Sciences","ror":"https://ror.org/01s57k749","country_code":"SG","type":"education","lineage":["https://openalex.org/I8696757"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"June Tay","raw_affiliation_strings":["School of Science and Technology, Singapore University of Social Sciences,Singapore"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Science and Technology, Singapore University of Social Sciences,Singapore","institution_ids":["https://openalex.org/I8696757"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I8696757"],"apc_list":null,"apc_paid":null,"fwci":2.7813,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.92155317,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1428","last_page":"1431"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11052","display_name":"Energy Load and Power Forecasting","score":0.9991999864578247,"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":0.9991999864578247,"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/T11954","display_name":"Energy Efficiency and Management","score":0.9896000027656555,"subfield":{"id":"https://openalex.org/subfields/2105","display_name":"Renewable Energy, Sustainability and the Environment"},"field":{"id":"https://openalex.org/fields/21","display_name":"Energy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10121","display_name":"Building Energy and Comfort Optimization","score":0.9624000191688538,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7140424251556396},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5636475086212158},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5576025247573853},{"id":"https://openalex.org/keywords/energy","display_name":"Energy (signal processing)","score":0.5258902907371521}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7140424251556396},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5636475086212158},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5576025247573853},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.5258902907371521},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tencon61640.2024.10903110","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tencon61640.2024.10903110","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"TENCON 2024 - 2024 IEEE Region 10 Conference (TENCON)","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":4,"referenced_works":["https://openalex.org/W2982616372","https://openalex.org/W3088296722","https://openalex.org/W4317600224","https://openalex.org/W4367625813"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W4387369504","https://openalex.org/W3046775127","https://openalex.org/W4394896187","https://openalex.org/W3170094116","https://openalex.org/W4386462264","https://openalex.org/W3107602296","https://openalex.org/W4364306694","https://openalex.org/W4312192474"],"abstract_inverted_index":{"The":[0,156,188],"rising":[1],"energy":[2,18,34,48,58,132,154,167,197],"costs":[3,198],"and":[4,36,73,90,100,122,175,184,208],"increasing":[5],"environmental":[6],"concerns":[7],"have":[8,79,104],"compelled":[9],"industries":[10,150],"to":[11,32,46,97,151,194,203],"seek":[12],"innovative":[13],"solutions":[14],"for":[15,130],"optimizing":[16],"their":[17],"consumption.":[19],"This":[20,52,135],"paper":[21,136],"presents":[22],"a":[23,37],"data-driven":[24],"approach":[25],"utilizing":[26],"Internet":[27],"of":[28,68,146,173,177],"Things":[29],"(IoT)":[30],"sensors":[31],"collect":[33],"data":[35,83],"comparative":[38],"study":[39],"on":[40,56,112],"various":[41,66,92,113,213],"time":[42,114],"series":[43,115],"forecasting":[44,49,116,168],"techniques":[45,85,96,125],"improve":[47,206],"in":[50,166,212],"industries.":[51],"experiment":[53],"is":[54],"based":[55,186],"public":[57],"usage":[59],"dataset":[60,129],"from":[61],"steel":[62,69],"company":[63],"which":[64],"produces":[65],"types":[67],"plates,":[70],"iron":[71],"plates":[72],"coils.":[74],"In":[75],"this":[76,128],"paper,":[77],"we":[78,103],"discussed":[80],"about":[81],"the":[82,106,143,147,192,201],"preprocessing":[84],"including":[86,139],"effective":[87],"feature":[88],"engineering":[89],"use":[91],"Machine":[93],"Learning":[94],"(ML)":[95],"identify":[98],"patterns":[99],"trends.":[101],"Also,":[102],"compared":[105],"results":[107,157],"with":[108,127,169,180],"previous":[109],"research":[110],"outcomes":[111],"models":[117,148,165],"such":[118],"as":[119],"deep":[120,182],"learning,":[121],"other":[123,164,181],"ensemble":[124],"tested":[126],"improved":[131],"consumption":[133],"forecasts.":[134],"shows":[137,158],"how":[138],"additional":[140],"features":[141],"improves":[142],"prediction":[144],"accuracy":[145],"allowing":[149],"achieve":[152],"optimal":[153],"utilization.":[155],"that":[159],"XGBoost":[160],"model":[161],"consistently":[162],"outperformed":[163],"lowest":[170],"RMSE":[171],"value":[172],"0.037":[174],"MAE":[176],"0.015":[178],"comparing":[179],"learning":[183],"tree":[185],"techniques.":[187],"proposed":[189],"methodology":[190],"has":[191],"potential":[193],"significantly":[195],"reduce":[196],"by":[199],"finding":[200],"area":[202],"save":[204],"energy,":[205],"sustainability,":[207],"enhance":[209],"operational":[210],"efficiency":[211],"industrial":[214],"sectors.":[215]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}