{"id":"https://openalex.org/W4214657682","doi":"https://doi.org/10.1109/access.2022.3153720","title":"Electrical Energy Prediction of Combined Cycle Power Plant Using Gradient Boosted Generalized Additive Model","display_name":"Electrical Energy Prediction of Combined Cycle Power Plant Using Gradient Boosted Generalized Additive Model","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W4214657682","doi":"https://doi.org/10.1109/access.2022.3153720"},"language":"en","primary_location":{"id":"doi:10.1109/access.2022.3153720","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2022.3153720","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/9668973/09718328.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/9668973/09718328.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5079131089","display_name":"Nikhil Pachauri","orcid":"https://orcid.org/0000-0003-2363-3129"},"institutions":[{"id":"https://openalex.org/I39534123","display_name":"Gwangju Institute of Science and Technology","ror":"https://ror.org/024kbgz78","country_code":"KR","type":"education","lineage":["https://openalex.org/I39534123"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Nikhil Pachauri","raw_affiliation_strings":["Artificial Intelligence Graduate School, Gwangju Institute of Science and Technology, Gwangju, South Korea"],"raw_orcid":"https://orcid.org/0000-0003-2363-3129","affiliations":[{"raw_affiliation_string":"Artificial Intelligence Graduate School, Gwangju Institute of Science and Technology, Gwangju, South Korea","institution_ids":["https://openalex.org/I39534123"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5028715246","display_name":"Chang Wook Ahn","orcid":"https://orcid.org/0000-0002-9902-5966"},"institutions":[{"id":"https://openalex.org/I39534123","display_name":"Gwangju Institute of Science and Technology","ror":"https://ror.org/024kbgz78","country_code":"KR","type":"education","lineage":["https://openalex.org/I39534123"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Chang Wook Ahn","raw_affiliation_strings":["Artificial Intelligence Graduate School, Gwangju Institute of Science and Technology, Gwangju, South Korea"],"raw_orcid":"https://orcid.org/0000-0002-9902-5966","affiliations":[{"raw_affiliation_string":"Artificial Intelligence Graduate School, Gwangju Institute of Science and Technology, Gwangju, South Korea","institution_ids":["https://openalex.org/I39534123"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5079131089"],"corresponding_institution_ids":["https://openalex.org/I39534123"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":1.3855,"has_fulltext":true,"cited_by_count":19,"citation_normalized_percentile":{"value":0.7987129,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":"10","issue":null,"first_page":"24566","last_page":"24577"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11052","display_name":"Energy Load and Power Forecasting","score":0.9976000189781189,"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":0.9976000189781189,"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/T10568","display_name":"Thermodynamic and Exergetic Analyses of Power and Cooling Systems","score":0.996399998664856,"subfield":{"id":"https://openalex.org/subfields/2210","display_name":"Mechanical 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/T11336","display_name":"Energy and Environment Impacts","score":0.9878000020980835,"subfield":{"id":"https://openalex.org/subfields/2310","display_name":"Pollution"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/multilayer-perceptron","display_name":"Multilayer perceptron","score":0.431056946516037},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4218469560146332},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3863704204559326},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.36248424649238586},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2677218019962311}],"concepts":[{"id":"https://openalex.org/C179717631","wikidata":"https://www.wikidata.org/wiki/Q2991667","display_name":"Multilayer perceptron","level":3,"score":0.431056946516037},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4218469560146332},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3863704204559326},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.36248424649238586},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2677218019962311}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2022.3153720","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2022.3153720","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/9668973/09718328.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:8108df9598ee4db1927fe40f8b00ad69","is_oa":true,"landing_page_url":"https://doaj.org/article/8108df9598ee4db1927fe40f8b00ad69","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 10, Pp 24566-24577 (2022)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2022.3153720","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2022.3153720","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/9668973/09718328.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Affordable and clean energy","score":0.8700000047683716,"id":"https://metadata.un.org/sdg/7"}],"awards":[{"id":"https://openalex.org/G1194156772","display_name":null,"funder_award_id":"2019-0-01842","funder_id":"https://openalex.org/F4320335489","funder_display_name":"Institute for Information and Communications Technology Promotion"},{"id":"https://openalex.org/G1994271716","display_name":null,"funder_award_id":"NRF-2021R1A2C3013687","funder_id":"https://openalex.org/F4320322120","funder_display_name":"National Research Foundation of Korea"},{"id":"https://openalex.org/G4627365436","display_name":null,"funder_award_id":"2021R1A2C3013687","funder_id":"https://openalex.org/F4320322120","funder_display_name":"National Research Foundation of Korea"},{"id":"https://openalex.org/G5705383526","display_name":null,"funder_award_id":"2019-0-01842","funder_id":"https://openalex.org/F4320321317","funder_display_name":"Gwangju Institute of Science and Technology"},{"id":"https://openalex.org/G6962897262","display_name":null,"funder_award_id":"2019-0-01842","funder_id":"https://openalex.org/F4320328359","funder_display_name":"Ministry of Science and ICT, South Korea"}],"funders":[{"id":"https://openalex.org/F4320320671","display_name":"National Research Foundation","ror":"https://ror.org/05s0g1g46"},{"id":"https://openalex.org/F4320321317","display_name":"Gwangju Institute of Science and Technology","ror":"https://ror.org/024kbgz78"},{"id":"https://openalex.org/F4320322120","display_name":"National Research Foundation of Korea","ror":"https://ror.org/013aysd81"},{"id":"https://openalex.org/F4320328359","display_name":"Ministry of Science and ICT, South Korea","ror":"https://ror.org/01wpjm123"},{"id":"https://openalex.org/F4320335489","display_name":"Institute for Information and Communications Technology Promotion","ror":"https://ror.org/01g0hqq23"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4214657682.pdf","grobid_xml":"https://content.openalex.org/works/W4214657682.grobid-xml"},"referenced_works_count":33,"referenced_works":["https://openalex.org/W1128200355","https://openalex.org/W1559338301","https://openalex.org/W1974875084","https://openalex.org/W1998987927","https://openalex.org/W2023728066","https://openalex.org/W2046945713","https://openalex.org/W2064769840","https://openalex.org/W2085008468","https://openalex.org/W2114234756","https://openalex.org/W2149033360","https://openalex.org/W2164459021","https://openalex.org/W2475877430","https://openalex.org/W2905217250","https://openalex.org/W2907866659","https://openalex.org/W2984841888","https://openalex.org/W3008014318","https://openalex.org/W3012378843","https://openalex.org/W3018966198","https://openalex.org/W3041628376","https://openalex.org/W3069920563","https://openalex.org/W3115002311","https://openalex.org/W3120740533","https://openalex.org/W3128323095","https://openalex.org/W3132057070","https://openalex.org/W3133745455","https://openalex.org/W3141121221","https://openalex.org/W3160768652","https://openalex.org/W3173215741","https://openalex.org/W3208906812","https://openalex.org/W4200565931","https://openalex.org/W4237210482","https://openalex.org/W4241615646","https://openalex.org/W4298870098"],"related_works":["https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W4391375266","https://openalex.org/W1979597421","https://openalex.org/W2007980826","https://openalex.org/W3158157485","https://openalex.org/W2789124470","https://openalex.org/W3000407446","https://openalex.org/W2116531472","https://openalex.org/W2103550798"],"abstract_inverted_index":{"A":[0],"combined":[1],"cycle":[2,25],"power":[3,15,63,94,231],"plant":[4,71,237],"(CCPP)":[5],"employs":[6],"gas":[7],"and":[8,47,73,107,116,126,153,176,185,196,222,239],"steam":[9],"turbines":[10],"to":[11,53,69,179,235],"generate":[12],"50&#x0025;":[13],"more":[14],"while":[16],"utilizing":[17],"the":[18,60,92,168,189,192,201,216,225,228],"same":[19],"fuel":[20],"as":[21,43,113],"a":[22,30,38,56,65,79,84,97,104,121,233],"normal":[23],"single":[24],"plant.":[26],"The":[27],"performance":[28],"of":[29,40,59,64,96,191,203,208,227,232],"CCPP":[31,66,98,234],"under":[32],"full":[33,100],"load":[34],"is":[35,67,89,219],"affected":[36],"by":[37,170],"variety":[39],"factors":[41],"such":[42],"weather,":[44],"process":[45,138],"interactions,":[46],"coupling,":[48],"which":[49],"makes":[50],"it":[51,211],"challenging":[52],"operate.":[54],"Therefore,":[55],"reliable":[57],"assessment":[58,226],"maximum":[61,229],"output":[62,127,230],"required":[68],"improve":[70,236],"reliability":[72],"monetary":[74],"performance.":[75,241],"In":[76,102],"this":[77],"paper,":[78],"predictive":[80,130],"model":[81,87],"based":[82,132],"on":[83,133],"generalized":[85],"additive":[86],"(GAM)":[88],"proposed":[90,217],"for":[91,119,160,205,224],"electrical":[93],"prediction":[95,207],"at":[99],"load.":[101],"GAM,":[103],"boosted":[105],"tree":[106,151,155],"gradient":[108],"boosting":[109],"algorithm":[110],"are":[111,157],"considered":[112],"shape":[114],"function":[115],"learning":[117],"technique":[118],"modeling":[120],"non-linear":[122],"relationship":[123],"between":[124],"input":[125],"attributes.":[128],"Furthermore,":[129,188],"models":[131],"linear":[134],"regression":[135,139,148],"(LR),":[136],"Gaussian":[137],"(GPR),":[140],"multilayer":[141],"perceptron":[142],"neural":[143],"network":[144],"(MLP),":[145],"support":[146],"vector":[147],"(SVR),":[149],"decision":[150],"(DT),":[152],"bootstrap-aggregated":[154],"(BBT)":[156],"also":[158,199],"designed":[159],"comparison":[161],"purposes.":[162],"Results":[163],"reveal":[164],"that":[165,215],"GAM":[166,204],"improves":[167],"RMSE":[169],"74&#x0025;,":[171],"68.8&#x0025;,":[172],"70.3&#x0025;,":[173],"54.8&#x0025;,":[174],"21.2&#x0025;,":[175],"17.3&#x0025;":[177],"compared":[178],"LR,":[180],"GPR,":[181],"MLP,":[182],"SVR,":[183],"DT,":[184],"BBT,":[186],"respectively.":[187],"results":[190],"Man-Whitney":[193],"U":[194],"test":[195],"rank":[197],"analysis":[198],"confirm":[200],"effectiveness":[202],"energy":[206],"CCPP.":[209],"Finally,":[210],"can":[212],"be":[213],"concluded":[214],"method":[218],"effective,":[220],"robust,":[221],"accurate":[223],"consistency":[238],"financial":[240]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
