{"id":"https://openalex.org/W4387870913","doi":"https://doi.org/10.1109/secon58729.2023.10287528","title":"ECM: An Energy-efficient HVAC Control Framework for Stable Construction Environment","display_name":"ECM: An Energy-efficient HVAC Control Framework for Stable Construction Environment","publication_year":2023,"publication_date":"2023-09-11","ids":{"openalex":"https://openalex.org/W4387870913","doi":"https://doi.org/10.1109/secon58729.2023.10287528"},"language":"en","primary_location":{"id":"doi:10.1109/secon58729.2023.10287528","is_oa":false,"landing_page_url":"https://doi.org/10.1109/secon58729.2023.10287528","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 20th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON)","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/A5008329510","display_name":"Jin-Sung Ok","orcid":null},"institutions":[{"id":"https://openalex.org/I4210143937","display_name":"Hanwha Solutions (South Korea)","ror":"https://ror.org/05dmq6f22","country_code":"KR","type":"company","lineage":["https://openalex.org/I4210143937","https://openalex.org/I4403386467"]},{"id":"https://openalex.org/I31419693","display_name":"Kyungpook National University","ror":"https://ror.org/040c17130","country_code":"KR","type":"education","lineage":["https://openalex.org/I31419693"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Jin-Sung Ok","raw_affiliation_strings":["Hanwha Ocean,Digital Solution R&#x0026;D Center,Geoje,Korea","School of Computer Science & Engineering, Kyungpook National University, Daegu, Korea"],"affiliations":[{"raw_affiliation_string":"Hanwha Ocean,Digital Solution R&#x0026;D Center,Geoje,Korea","institution_ids":["https://openalex.org/I4210143937"]},{"raw_affiliation_string":"School of Computer Science & Engineering, Kyungpook National University, Daegu, Korea","institution_ids":["https://openalex.org/I31419693"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5025444920","display_name":"Youngeun Chae","orcid":null},"institutions":[{"id":"https://openalex.org/I31419693","display_name":"Kyungpook National University","ror":"https://ror.org/040c17130","country_code":"KR","type":"education","lineage":["https://openalex.org/I31419693"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Youngeun Chae","raw_affiliation_strings":["Kyungpook National University,School of Computer Science &#x0026; Engineering,Daegu,Korea"],"affiliations":[{"raw_affiliation_string":"Kyungpook National University,School of Computer Science &#x0026; Engineering,Daegu,Korea","institution_ids":["https://openalex.org/I31419693"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109649147","display_name":"Harin Seo","orcid":null},"institutions":[{"id":"https://openalex.org/I31419693","display_name":"Kyungpook National University","ror":"https://ror.org/040c17130","country_code":"KR","type":"education","lineage":["https://openalex.org/I31419693"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Harin Seo","raw_affiliation_strings":["Kyungpook National University,School of Computer Science &#x0026; Engineering,Daegu,Korea"],"affiliations":[{"raw_affiliation_string":"Kyungpook National University,School of Computer Science &#x0026; Engineering,Daegu,Korea","institution_ids":["https://openalex.org/I31419693"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086903003","display_name":"Soon-Do Kwon","orcid":null},"institutions":[{"id":"https://openalex.org/I4210143937","display_name":"Hanwha Solutions (South Korea)","ror":"https://ror.org/05dmq6f22","country_code":"KR","type":"company","lineage":["https://openalex.org/I4210143937","https://openalex.org/I4403386467"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Soon-Do Kwon","raw_affiliation_strings":["Hanwha Ocean,Digital Solution R&#x0026;D Center,Geoje,Korea"],"affiliations":[{"raw_affiliation_string":"Hanwha Ocean,Digital Solution R&#x0026;D Center,Geoje,Korea","institution_ids":["https://openalex.org/I4210143937"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010776108","display_name":"Byungchul Tak","orcid":"https://orcid.org/0000-0002-8204-6816"},"institutions":[{"id":"https://openalex.org/I31419693","display_name":"Kyungpook National University","ror":"https://ror.org/040c17130","country_code":"KR","type":"education","lineage":["https://openalex.org/I31419693"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Byungchul Tak","raw_affiliation_strings":["Kyungpook National University,School of Computer Science &#x0026; Engineering,Daegu,Korea"],"affiliations":[{"raw_affiliation_string":"Kyungpook National University,School of Computer Science &#x0026; Engineering,Daegu,Korea","institution_ids":["https://openalex.org/I31419693"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101626811","display_name":"Young\u2010Kyoon Suh","orcid":"https://orcid.org/0000-0003-3124-2566"},"institutions":[{"id":"https://openalex.org/I31419693","display_name":"Kyungpook National University","ror":"https://ror.org/040c17130","country_code":"KR","type":"education","lineage":["https://openalex.org/I31419693"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Young-Kyoon Suh","raw_affiliation_strings":["Kyungpook National University,School of Computer Science &#x0026; Engineering,Daegu,Korea"],"affiliations":[{"raw_affiliation_string":"Kyungpook National University,School of Computer Science &#x0026; Engineering,Daegu,Korea","institution_ids":["https://openalex.org/I31419693"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5008329510"],"corresponding_institution_ids":["https://openalex.org/I31419693","https://openalex.org/I4210143937"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.13226772,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"249","last_page":"257"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11371","display_name":"Wind and Air Flow Studies","score":0.996999979019165,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11371","display_name":"Wind and Air Flow Studies","score":0.996999979019165,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10121","display_name":"Building Energy and Comfort Optimization","score":0.9944000244140625,"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"}},{"id":"https://openalex.org/T11052","display_name":"Energy Load and Power Forecasting","score":0.9941999912261963,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/hvac","display_name":"HVAC","score":0.9222491979598999},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.5484954118728638},{"id":"https://openalex.org/keywords/air-conditioning","display_name":"Air conditioning","score":0.5389379262924194},{"id":"https://openalex.org/keywords/component","display_name":"Component (thermodynamics)","score":0.5136613249778748},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.507677435874939},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.4238070845603943},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4110445976257324},{"id":"https://openalex.org/keywords/automotive-engineering","display_name":"Automotive engineering","score":0.33878305554389954},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.31057655811309814},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2771438658237457},{"id":"https://openalex.org/keywords/mechanical-engineering","display_name":"Mechanical engineering","score":0.16128462553024292},{"id":"https://openalex.org/keywords/operating-system","display_name":"Operating system","score":0.08987385034561157}],"concepts":[{"id":"https://openalex.org/C122346748","wikidata":"https://www.wikidata.org/wiki/Q1798773","display_name":"HVAC","level":3,"score":0.9222491979598999},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.5484954118728638},{"id":"https://openalex.org/C103742991","wikidata":"https://www.wikidata.org/wiki/Q173725","display_name":"Air conditioning","level":2,"score":0.5389379262924194},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.5136613249778748},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.507677435874939},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.4238070845603943},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4110445976257324},{"id":"https://openalex.org/C171146098","wikidata":"https://www.wikidata.org/wiki/Q124192","display_name":"Automotive engineering","level":1,"score":0.33878305554389954},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.31057655811309814},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2771438658237457},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.16128462553024292},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.08987385034561157},{"id":"https://openalex.org/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","level":1,"score":0.0},{"id":"https://openalex.org/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","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/secon58729.2023.10287528","is_oa":false,"landing_page_url":"https://doi.org/10.1109/secon58729.2023.10287528","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 20th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320311649","display_name":"Ministry of Education","ror":"https://ror.org/036nq5137"},{"id":"https://openalex.org/F4320320671","display_name":"National Research Foundation","ror":"https://ror.org/05s0g1g46"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":37,"referenced_works":["https://openalex.org/W1924770834","https://openalex.org/W2004743305","https://openalex.org/W2064675550","https://openalex.org/W2076420009","https://openalex.org/W2130382978","https://openalex.org/W2145339207","https://openalex.org/W2170892587","https://openalex.org/W2295598076","https://openalex.org/W2498029196","https://openalex.org/W2563700852","https://openalex.org/W2739564999","https://openalex.org/W2746553466","https://openalex.org/W2790938860","https://openalex.org/W2951799221","https://openalex.org/W2985363569","https://openalex.org/W3006459398","https://openalex.org/W3011863222","https://openalex.org/W3018700377","https://openalex.org/W3024740098","https://openalex.org/W3028766998","https://openalex.org/W3033324992","https://openalex.org/W3044030690","https://openalex.org/W3045748774","https://openalex.org/W3049184217","https://openalex.org/W3094462455","https://openalex.org/W3172795845","https://openalex.org/W3201919935","https://openalex.org/W3202525453","https://openalex.org/W3216599946","https://openalex.org/W4234971943","https://openalex.org/W4297934835","https://openalex.org/W4298857966","https://openalex.org/W6637967152","https://openalex.org/W6640212811","https://openalex.org/W6685444567","https://openalex.org/W6777656069","https://openalex.org/W6779265984"],"related_works":["https://openalex.org/W2112866972","https://openalex.org/W4240233711","https://openalex.org/W2900606913","https://openalex.org/W4320003279","https://openalex.org/W2326910963","https://openalex.org/W3111008797","https://openalex.org/W3046823714","https://openalex.org/W2348218441","https://openalex.org/W3021806226","https://openalex.org/W4205684936"],"abstract_inverted_index":{"A":[0],"cargo":[1],"containment":[2],"system":[3],"(CCS)":[4],"of":[5,14,23,82,93,161,172,234],"liquefied":[6],"natural":[7],"gas":[8],"(LNG)":[9],"is":[10,28,64],"an":[11,15,127,231],"essential":[12],"component":[13],"LNG":[16],"carrier":[17],"(LNGC).":[18],"During":[19],"the":[20,24,31,40,44,58,107,116,133,137,142,148,159,165,173,188,193,196,203,221,244],"manufacturing":[21],"process":[22],"LNGC":[25,59],"CCS,":[26],"it":[27,66],"critical":[29,62],"that":[30,65,89,140,182],"heating,":[32],"ventilation,":[33],"and":[34,53,96,104,125,145],"air":[35],"conditioning":[36],"(HVAC)":[37],"facility":[38],"stabilizes":[39],"environmental":[41],"states":[42],"inside":[43,57,195],"CCS":[45,197],"at":[46],"all":[47],"times":[48],"to":[49,102,208,216],"prevent":[50],"devastating":[51],"rust":[52],"dew":[54],"from":[55,121,170,206],"forming":[56],"CCS.":[60],"One":[61],"problem":[63],"consumes":[67],"enormous":[68],"power,":[69],"resulting":[70],"in":[71,187,202,243],"high":[72],"expenses.":[73],"To":[74,151],"alleviate":[75],"this":[76],"problem,":[77],"we":[78,114,156,180],"propose":[79],"our":[80,153,162,183,227],"design":[81],"a":[83,91,178],"novel":[84],"data-driven":[85],"framework,":[86,155],"termed":[87],"ECM,":[88],"uses":[90,141],"combination":[92],"machine":[94],"learning":[95,99],"deep":[97],"reinforcement":[98],"(DRL)":[100],"models":[101,124,163],"robustly":[103],"automatically":[105],"control":[106,129],"HVAC":[108,128],"system.":[109],"Based":[110],"on":[111,164,211],"selected":[112],"features,":[113],"develop":[115],"best":[117],"indoor-environment":[118],"forecasting":[119,149],"model":[120,135,185],"several":[122],"candidate":[123],"build":[126],"agent":[130],"by":[131],"training":[132],"DRL":[134],"with":[136],"reward":[138],"function":[139],"predicted":[143],"temperature":[144,194],"humidity":[146],"through":[147],"model.":[150],"validate":[152],"proposed":[154,189],"have":[157],"assessed":[158],"performance":[160],"real-world":[166],"sensor":[167],"data":[168],"obtained":[169],"one":[171],"major":[174],"world-class":[175],"shipyards.":[176],"As":[177],"result,":[179],"show":[181],"DRL-based":[184],"trained":[186],"framework":[190,228],"stably":[191],"controls":[192],"within":[198],"only":[199],"1.$5^{\\mathrm{o}}$C":[200],"variance":[201],"set":[204],"range":[205],"2$3^{\\mathrm{o}}$C":[207],"2$5^{\\mathrm{o}}$C":[209],"while":[210],"average":[212],"consuming":[213],"power":[214],"up":[215],"about":[217,235],"34%":[218],"less":[219],"than":[220],"compared":[222],"existing":[223],"methods.":[224],"We":[225],"expect":[226],"will":[229],"bring":[230],"annual":[232],"savings":[233],"${\\$}$":[236],"14":[237],"million":[238],"or":[239],"more":[240],"once":[241],"deployed":[242],"actual":[245],"field.":[246]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
