{"id":"https://openalex.org/W4415328242","doi":"https://doi.org/10.1016/j.egyr.2025.10.002","title":"A new method for predicting carbon emissions in energy industry based on CEEMD wavelet denoising and hybrid machine learning","display_name":"A new method for predicting carbon emissions in energy industry based on CEEMD wavelet denoising and hybrid machine learning","publication_year":2025,"publication_date":"2025-10-18","ids":{"openalex":"https://openalex.org/W4415328242","doi":"https://doi.org/10.1016/j.egyr.2025.10.002"},"language":"en","primary_location":{"id":"doi:10.1016/j.egyr.2025.10.002","doi":"https://doi.org/10.1016/j.egyr.2025.10.002","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.egyr.2025.10.002","pdf_url":null,"source":{"id":"https://openalex.org/S2898434319","display_name":"Energy Reports","issn_l":"2352-4847","issn":["2352-4847"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["cwts-core","doaj","dongbi-b","jufo-1","norway-1","oarl","poland-100","vabb-shw"],"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Energy Reports","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1016/j.egyr.2025.10.002","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5078541685","display_name":"Dongge Zhu","orcid":"https://orcid.org/0009-0003-7098-1203","observed_orcids":["https://orcid.org/0009-0003-7098-1203"]},"institutions":[{"id":"https://openalex.org/I17442442","display_name":"State Grid Corporation of China (China)","ror":"https://ror.org/05twwhs70","country_code":"CN","type":"company","lineage":["https://openalex.org/I17442442"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Dongge Zhu","raw_affiliation_strings":["State Grid Ningxia Electric Power Co., Ltd. Technical Research Institute, Yinchuan, Ningxia, 750002, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"State Grid Ningxia Electric Power Co., Ltd. Technical Research Institute, Yinchuan, Ningxia, 750002, China","institution_ids":["https://openalex.org/I17442442"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009578306","display_name":"Rui Ma","orcid":"https://orcid.org/0000-0002-4962-4776","observed_orcids":["https://orcid.org/0000-0002-4962-4776"]},"institutions":[{"id":"https://openalex.org/I17442442","display_name":"State Grid Corporation of China (China)","ror":"https://ror.org/05twwhs70","country_code":"CN","type":"company","lineage":["https://openalex.org/I17442442"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Rui Ma","raw_affiliation_strings":["State Grid Ningxia Electric Power Co., Ltd. Technical Research Institute, Yinchuan, Ningxia, 750002, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"State Grid Ningxia Electric Power Co., Ltd. Technical Research Institute, Yinchuan, Ningxia, 750002, China","institution_ids":["https://openalex.org/I17442442"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111090299","display_name":"Jia Liu","orcid":"https://orcid.org/0009-0002-3963-8661","observed_orcids":["https://orcid.org/0009-0002-3963-8661"]},"institutions":[{"id":"https://openalex.org/I17442442","display_name":"State Grid Corporation of China (China)","ror":"https://ror.org/05twwhs70","country_code":"CN","type":"company","lineage":["https://openalex.org/I17442442"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jia Liu","raw_affiliation_strings":["State Grid Ningxia Electric Power Co., Ltd. Technical Research Institute, Yinchuan, Ningxia, 750002, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"State Grid Ningxia Electric Power Co., Ltd. Technical Research Institute, Yinchuan, Ningxia, 750002, China","institution_ids":["https://openalex.org/I17442442"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100672848","display_name":"Xinghua Li","orcid":"https://orcid.org/0000-0002-3275-2755","observed_orcids":["https://orcid.org/0000-0002-3275-2755","https://orcid.org/0000-0002-6276-1468"]},"institutions":[{"id":"https://openalex.org/I17442442","display_name":"State Grid Corporation of China (China)","ror":"https://ror.org/05twwhs70","country_code":"CN","type":"company","lineage":["https://openalex.org/I17442442"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinghua Li","raw_affiliation_strings":["State Grid Ningxia Electric Power Co., Ltd. Technical Research Institute, Yinchuan, Ningxia, 750002, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"State Grid Ningxia Electric Power Co., Ltd. Technical Research Institute, Yinchuan, Ningxia, 750002, China","institution_ids":["https://openalex.org/I17442442"]}]},{"author_position":"last","author":{"id":null,"display_name":"Jiangbo Sha","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I17442442","display_name":"State Grid Corporation of China (China)","ror":"https://ror.org/05twwhs70","country_code":"CN","type":"company","lineage":["https://openalex.org/I17442442"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiangbo Sha","raw_affiliation_strings":["State Grid Ningxia Electric Power Co., Ltd. Technical Research Institute, Yinchuan, Ningxia, 750002, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"State Grid Ningxia Electric Power Co., Ltd. Technical Research Institute, Yinchuan, Ningxia, 750002, China","institution_ids":["https://openalex.org/I17442442"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5078541685"],"corresponding_institution_ids":["https://openalex.org/I17442442"],"apc_list":{"value":3040,"currency":"USD","value_usd":3040},"apc_paid":{"value":3040,"currency":"USD","value_usd":3040},"fwci":4.7308,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.9501804,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":"14","issue":null,"first_page":"3132","last_page":"3141"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10438","display_name":"Energy, Environment, Economic Growth","score":0.30959999561309814,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T10438","display_name":"Energy, Environment, Economic Growth","score":0.30959999561309814,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11052","display_name":"Energy Load and Power Forecasting","score":0.1477999985218048,"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/T11326","display_name":"Stock Market Forecasting Methods","score":0.1168999969959259,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/carbon-emissions-forecasting","display_name":"carbon emission prediction","score":0.953000009059906},{"id":"https://openalex.org/keywords/energy-sector","display_name":"energy sector","score":0.9539999961853027},{"id":"https://openalex.org/keywords/complementary-ensemble-empirical-mode-decomposition","display_name":"complementary ensemble empirical mode decomposition","score":0.8180000185966492},{"id":"https://openalex.org/keywords/wavelet-denoising","display_name":"wavelet denoising","score":0.8460000157356262},{"id":"https://openalex.org/keywords/wavelet-thresholding","display_name":"wavelet thresholding","score":0.9210000038146973},{"id":"https://openalex.org/keywords/intrinsic-mode-function","display_name":"intrinsic mode functions","score":0.9100000262260437},{"id":"https://openalex.org/keywords/nonlinear-autoregressive-neural-network","display_name":"nonlinear autoregressive neural network","score":0.9120000004768372},{"id":"https://openalex.org/keywords/deep-extreme-learning-machine","display_name":"deep extreme learning machine","score":0.8889999985694885},{"id":"https://openalex.org/keywords/hybrid-machine-learning","display_name":"hybrid machine learning","score":0.7799999713897705},{"id":"https://openalex.org/keywords/ensemble-empirical-mode-decomposition","display_name":"ensemble empirical mode decomposition","score":0.7250000238418579},{"id":"https://openalex.org/keywords/data-denoising","display_name":"data denoising","score":0.5619999766349792}],"concepts":[{"id":"https://openalex.org/C25570617","wikidata":"https://www.wikidata.org/wiki/Q1006462","display_name":"Hilbert\u2013Huang transform","level":3,"score":0.6912000179290771},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5914999842643738},{"id":"https://openalex.org/C2780150128","wikidata":"https://www.wikidata.org/wiki/Q21948731","display_name":"Extreme learning machine","level":3,"score":0.5853000283241272},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5842999815940857},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.5806000232696533},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.5521000027656555},{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.5234000086784363},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.5109999775886536},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5031999945640564},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.49810001254081726},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4408000111579895},{"id":"https://openalex.org/C196216189","wikidata":"https://www.wikidata.org/wiki/Q2867","display_name":"Wavelet transform","level":3,"score":0.44040000438690186},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.43799999356269836},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.43050000071525574},{"id":"https://openalex.org/C42536954","wikidata":"https://www.wikidata.org/wiki/Q7049462","display_name":"Nonlinear autoregressive exogenous model","level":3,"score":0.39980000257492065},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.3871000111103058},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.321399986743927},{"id":"https://openalex.org/C179717631","wikidata":"https://www.wikidata.org/wiki/Q2991667","display_name":"Multilayer perceptron","level":3,"score":0.31029999256134033},{"id":"https://openalex.org/C46286280","wikidata":"https://www.wikidata.org/wiki/Q2414958","display_name":"Discrete wavelet transform","level":4,"score":0.3070000112056732},{"id":"https://openalex.org/C48677424","wikidata":"https://www.wikidata.org/wiki/Q6888088","display_name":"Mode (computer interface)","level":2,"score":0.299699991941452},{"id":"https://openalex.org/C140205800","wikidata":"https://www.wikidata.org/wiki/Q5860","display_name":"Carbon fibers","level":3,"score":0.28859999775886536},{"id":"https://openalex.org/C174598085","wikidata":"https://www.wikidata.org/wiki/Q746673","display_name":"Acoustic emission","level":2,"score":0.27090001106262207},{"id":"https://openalex.org/C60908668","wikidata":"https://www.wikidata.org/wiki/Q690207","display_name":"Perceptron","level":3,"score":0.2687000036239624},{"id":"https://openalex.org/C2779200991","wikidata":"https://www.wikidata.org/wiki/Q20681009","display_name":"Carbon price","level":3,"score":0.2669000029563904},{"id":"https://openalex.org/C16910744","wikidata":"https://www.wikidata.org/wiki/Q7705759","display_name":"Test data","level":2,"score":0.26649999618530273},{"id":"https://openalex.org/C155777637","wikidata":"https://www.wikidata.org/wiki/Q2736187","display_name":"Wavelet packet decomposition","level":4,"score":0.25450000166893005},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.25130000710487366}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1016/j.egyr.2025.10.002","doi":"https://doi.org/10.1016/j.egyr.2025.10.002","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.egyr.2025.10.002","pdf_url":null,"source":{"id":"https://openalex.org/S2898434319","display_name":"Energy Reports","issn_l":"2352-4847","issn":["2352-4847"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["cwts-core","doaj","dongbi-b","jufo-1","norway-1","oarl","poland-100","vabb-shw"],"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Energy Reports","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1016/j.egyr.2025.10.002","doi":"https://doi.org/10.1016/j.egyr.2025.10.002","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.egyr.2025.10.002","pdf_url":null,"source":{"id":"https://openalex.org/S2898434319","display_name":"Energy Reports","issn_l":"2352-4847","issn":["2352-4847"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["cwts-core","doaj","dongbi-b","jufo-1","norway-1","oarl","poland-100","vabb-shw"],"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Energy Reports","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/13","display_name":"Climate action","score":1.0},{"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy","score":0.5690000057220459},{"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure","score":0.4943999946117401}],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W3198574663","https://openalex.org/W3209703903","https://openalex.org/W3217518391","https://openalex.org/W4205302371","https://openalex.org/W4206966816","https://openalex.org/W4207028959","https://openalex.org/W4210752071","https://openalex.org/W4213256309","https://openalex.org/W4214829282","https://openalex.org/W4221009010","https://openalex.org/W4221164408","https://openalex.org/W4224608754","https://openalex.org/W4224903947","https://openalex.org/W4225826617","https://openalex.org/W4303684782","https://openalex.org/W4309199397","https://openalex.org/W4311498587","https://openalex.org/W4313487625","https://openalex.org/W4376057452"],"related_works":["https://openalex.org/W4291520172","https://openalex.org/W4389783750","https://openalex.org/W4376127540","https://openalex.org/W4392002316","https://openalex.org/W4416614474","https://openalex.org/W4399530867","https://openalex.org/W4399529916","https://openalex.org/W4393106665","https://openalex.org/W4386483292","https://openalex.org/W4400532374"],"abstract_inverted_index":{"As":[0],"an":[1],"important":[2],"source":[3],"of":[4,20,198],"carbon":[5,39,64,143,168,187,211,233],"emissions":[6,40],"in":[7,41,192,232],"contemporary":[8],"society,":[9],"the":[10,17,42,68,72,81,84,102,126,132,141,172,185,193,199,204],"energy":[11,43,69,173],"industry":[12,44,70],"is":[13,45,86,99,118,151,190,207],"currently":[14],"affected":[15],"by":[16],"redundant":[18],"impact":[19],"massive":[21],"data,":[22],"which":[23],"generates":[24],"significant":[25],"noise":[26],"and":[27,57,77,125,137,160,184,195,229],"reduces":[28,226],"prediction":[29,82,170,177,201],"accuracy.":[30],"To":[31],"address":[32],"this,":[33],"a":[34,89,154,161],"new":[35],"method":[36,93,117,218],"for":[37,80,171],"predicting":[38],"proposed,":[46],"based":[47,94],"on":[48,95],"Complementary":[49],"Ensemble":[50],"Empirical":[51],"Mode":[52,110],"Decomposition":[53],"(CEEMD),":[54],"wavelet":[55,90,115],"denoising,":[56],"hybrid":[58,147],"machine":[59,148,165],"learning.":[60],"By":[61],"collecting":[62],"historical":[63],"emission":[65,144,169,188,234],"data":[66,85],"from":[67],"as":[71],"training":[73,194],"set,":[74,76],"test":[75],"validation":[78],"set":[79],"model,":[83],"denoised":[87,127,142,186],"using":[88,101,153],"threshold":[91],"denoising":[92,116],"CEEMD.":[96],"The":[97,114,175],"dataset":[98,189],"decomposed":[100],"CEEMD":[103],"algorithm":[104],"to":[105,121,139,166,209],"obtain":[106,140],"several":[107],"noisy":[108],"Intrinsic":[109],"Function":[111],"(IMF)":[112],"components.":[113],"then":[119],"applied":[120,208],"each":[122],"IMF":[123,128],"component,":[124],"components":[129],"along":[130],"with":[131],"residual":[133],"component":[134],"are":[135,179],"combined":[136,180,200],"reconstructed":[138],"dataset.":[145],"A":[146],"learning":[149,164],"model":[150,206],"constructed":[152],"Nonlinear":[155],"Autoregressive":[156],"(NAR)":[157],"neural":[158],"network":[159],"deep":[162],"extreme":[163],"achieve":[167],"industry.":[174],"two":[176],"models":[178],"through":[181],"optimal":[182],"weighting,":[183],"used":[191],"testing":[196],"phases":[197],"model.":[202],"Finally,":[203],"trained":[205],"predict":[210],"emissions.":[212],"Experimental":[213],"analysis":[214],"demonstrates":[215],"that":[216],"this":[217],"achieves":[219],"higher":[220],"accuracy,":[221],"exhibits":[222],"good":[223],"stability,":[224],"effectively":[225],"error":[227],"fluctuations,":[228],"performs":[230],"excellently":[231],"prediction.":[235]},"counts_by_year":[{"year":2026,"cited_by_count":6},{"year":2025,"cited_by_count":1}],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2025-10-19T00:00:00"}