{"id":"https://openalex.org/W4411087685","doi":"https://doi.org/10.1016/j.engappai.2025.111380","title":"Power generation forecasting based on hybrid deep learning models for a multi-energy power generation system","display_name":"Power generation forecasting based on hybrid deep learning models for a multi-energy power generation system","publication_year":2025,"publication_date":"2025-06-06","ids":{"openalex":"https://openalex.org/W4411087685","doi":"https://doi.org/10.1016/j.engappai.2025.111380"},"language":"en","primary_location":{"id":"doi:10.1016/j.engappai.2025.111380","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.engappai.2025.111380","pdf_url":null,"source":{"id":"https://openalex.org/S900972176","display_name":"Engineering Applications of Artificial Intelligence","issn_l":"0952-1976","issn":["0952-1976","1873-6769"],"is_oa":false,"is_in_doaj":false,"is_core":true,"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":"Engineering Applications of Artificial Intelligence","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://doi.org/10.1016/j.engappai.2025.111380","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5053706952","display_name":"Ying Song","orcid":"https://orcid.org/0000-0002-6704-2040"},"institutions":[{"id":"https://openalex.org/I134687103","display_name":"Nanjing Tech University","ror":"https://ror.org/03sd35x91","country_code":"CN","type":"education","lineage":["https://openalex.org/I134687103"]},{"id":"https://openalex.org/I2799736854","display_name":"Nanjing Institute of Technology","ror":"https://ror.org/00n6txq60","country_code":"CN","type":"education","lineage":["https://openalex.org/I2799736854"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ying Song","raw_affiliation_strings":["School of Energy and Power Engineering, Nanjing Institute of Technology, Nanjing, 211167, China","School of Mechanical and Power Engineering, Nanjing Tech University, Nanjing, 211816, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Energy and Power Engineering, Nanjing Institute of Technology, Nanjing, 211167, China","institution_ids":["https://openalex.org/I2799736854"]},{"raw_affiliation_string":"School of Mechanical and Power Engineering, Nanjing Tech University, Nanjing, 211816, China","institution_ids":["https://openalex.org/I134687103"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080044037","display_name":"Jianqiu Zhou","orcid":"https://orcid.org/0000-0002-7381-9514"},"institutions":[{"id":"https://openalex.org/I134687103","display_name":"Nanjing Tech University","ror":"https://ror.org/03sd35x91","country_code":"CN","type":"education","lineage":["https://openalex.org/I134687103"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jianqiu Zhou","raw_affiliation_strings":["School of Mechanical and Power Engineering, Nanjing Tech University, Nanjing, 211816, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Mechanical and Power Engineering, Nanjing Tech University, Nanjing, 211816, China","institution_ids":["https://openalex.org/I134687103"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100765006","display_name":"Peng Yu","orcid":"https://orcid.org/0000-0003-1089-6421"},"institutions":[{"id":"https://openalex.org/I2799736854","display_name":"Nanjing Institute of Technology","ror":"https://ror.org/00n6txq60","country_code":"CN","type":"education","lineage":["https://openalex.org/I2799736854"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yu Peng","raw_affiliation_strings":["School of Energy and Power Engineering, Nanjing Institute of Technology, Nanjing, 211167, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Energy and Power Engineering, Nanjing Institute of Technology, Nanjing, 211167, China","institution_ids":["https://openalex.org/I2799736854"]}]},{"author_position":"last","author":{"id":null,"display_name":"Jin Yan","orcid":null},"institutions":[{"id":"https://openalex.org/I2799736854","display_name":"Nanjing Institute of Technology","ror":"https://ror.org/00n6txq60","country_code":"CN","type":"education","lineage":["https://openalex.org/I2799736854"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jin Yan","raw_affiliation_strings":["School of Energy and Power Engineering, Nanjing Institute of Technology, Nanjing, 211167, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Energy and Power Engineering, Nanjing Institute of Technology, Nanjing, 211167, China","institution_ids":["https://openalex.org/I2799736854"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5080044037"],"corresponding_institution_ids":["https://openalex.org/I134687103"],"apc_list":{"value":3170,"currency":"USD","value_usd":3170},"apc_paid":{"value":3170,"currency":"USD","value_usd":3170},"fwci":4.4701,"has_fulltext":false,"cited_by_count":14,"citation_normalized_percentile":{"value":0.95364871,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":100},"biblio":{"volume":"157","issue":null,"first_page":"111380","last_page":"111380"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11052","display_name":"Energy Load and Power Forecasting","score":0.9993000030517578,"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":0.9993000030517578,"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/T13891","display_name":"Engineering Diagnostics and Reliability","score":0.9490000009536743,"subfield":{"id":"https://openalex.org/subfields/2211","display_name":"Mechanics of Materials"},"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/T12451","display_name":"Smart Grid and Power Systems","score":0.9376000165939331,"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/computer-science","display_name":"Computer science","score":0.8894921541213989},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.583100438117981},{"id":"https://openalex.org/keywords/power","display_name":"Power (physics)","score":0.531208872795105},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5055961608886719},{"id":"https://openalex.org/keywords/energy","display_name":"Energy (signal processing)","score":0.43100571632385254},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.39791420102119446}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8894921541213989},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.583100438117981},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.531208872795105},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5055961608886719},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.43100571632385254},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39791420102119446},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","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/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1016/j.engappai.2025.111380","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.engappai.2025.111380","pdf_url":null,"source":{"id":"https://openalex.org/S900972176","display_name":"Engineering Applications of Artificial Intelligence","issn_l":"0952-1976","issn":["0952-1976","1873-6769"],"is_oa":false,"is_in_doaj":false,"is_core":true,"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":"Engineering Applications of Artificial Intelligence","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1016/j.engappai.2025.111380","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.engappai.2025.111380","pdf_url":null,"source":{"id":"https://openalex.org/S900972176","display_name":"Engineering Applications of Artificial Intelligence","issn_l":"0952-1976","issn":["0952-1976","1873-6769"],"is_oa":false,"is_in_doaj":false,"is_core":true,"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":"Engineering Applications of Artificial Intelligence","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy","score":0.8600000143051147}],"awards":[{"id":"https://openalex.org/G3047424127","display_name":null,"funder_award_id":"22KJB470012","funder_id":"https://openalex.org/F4320335440","funder_display_name":"Natural Science Research of Jiangsu Higher Education Institutions of China"},{"id":"https://openalex.org/G4739834344","display_name":null,"funder_award_id":"BK20230700","funder_id":"https://openalex.org/F4320322769","funder_display_name":"Natural Science Foundation of Jiangsu Province"},{"id":"https://openalex.org/G7567894069","display_name":null,"funder_award_id":"12305186","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320322769","display_name":"Natural Science Foundation of Jiangsu Province","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320327891","display_name":"Major Basic Research Project of the Natural Science Foundation of the Jiangsu Higher Education Institutions","ror":null},{"id":"https://openalex.org/F4320335440","display_name":"Natural Science Research of Jiangsu Higher Education Institutions of China","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":65,"referenced_works":["https://openalex.org/W1968315006","https://openalex.org/W2098395403","https://openalex.org/W2164117960","https://openalex.org/W2178310074","https://openalex.org/W2329476579","https://openalex.org/W2558906580","https://openalex.org/W2604864721","https://openalex.org/W2774997576","https://openalex.org/W2801076518","https://openalex.org/W2897169932","https://openalex.org/W2900921197","https://openalex.org/W2919115771","https://openalex.org/W2953231870","https://openalex.org/W2978345470","https://openalex.org/W3036860367","https://openalex.org/W3088857080","https://openalex.org/W3158846216","https://openalex.org/W3166156627","https://openalex.org/W3204131182","https://openalex.org/W4212880590","https://openalex.org/W4225243270","https://openalex.org/W4280651865","https://openalex.org/W4285096900","https://openalex.org/W4288810157","https://openalex.org/W4292537033","https://openalex.org/W4311303625","https://openalex.org/W4311758695","https://openalex.org/W4321485251","https://openalex.org/W4377234304","https://openalex.org/W4382583402","https://openalex.org/W4386783337","https://openalex.org/W4387733210","https://openalex.org/W4389559682","https://openalex.org/W4390128809","https://openalex.org/W4390278427","https://openalex.org/W4390446398","https://openalex.org/W4390628543","https://openalex.org/W4390776100","https://openalex.org/W4391074144","https://openalex.org/W4391352826","https://openalex.org/W4391430480","https://openalex.org/W4392821502","https://openalex.org/W4393176994","https://openalex.org/W4395477921","https://openalex.org/W4400118104","https://openalex.org/W4400959831","https://openalex.org/W4401397468","https://openalex.org/W4404643816","https://openalex.org/W4404906945","https://openalex.org/W4405109230","https://openalex.org/W4405734549","https://openalex.org/W4406240619","https://openalex.org/W4406864694","https://openalex.org/W4407291270","https://openalex.org/W6678540043","https://openalex.org/W6796630073","https://openalex.org/W6810233076","https://openalex.org/W6847480943","https://openalex.org/W6854352468","https://openalex.org/W6859317659","https://openalex.org/W6859988638","https://openalex.org/W6859998438","https://openalex.org/W6862663565","https://openalex.org/W6871158878","https://openalex.org/W6875356736"],"related_works":["https://openalex.org/W2731899572","https://openalex.org/W2961085424","https://openalex.org/W3215138031","https://openalex.org/W4306674287","https://openalex.org/W3009238340","https://openalex.org/W4360585206","https://openalex.org/W4321369474","https://openalex.org/W4285208911","https://openalex.org/W4387369504","https://openalex.org/W3082895349"],"abstract_inverted_index":{"The":[0,77],"accurate":[1],"power":[2,10,46],"generation":[3,11,47],"forecasting":[4,38,187],"(PGF)":[5],"of":[6,39,139,146,153,159,167,178],"the":[7,19,37,40,43,53,57,81,114,130,134,176,184],"multi-energy":[8,44],"complementary":[9,45],"system":[12,48],"(MECPG)":[13],"is":[14,50,133],"great":[15],"significance":[16],"which":[17,49,69,182],"lays":[18],"foundation":[20],"for":[21,36,94,105,137,175,191],"coordinated":[22],"energy":[23],"matching":[24],"and":[25,56,66,100,116,120,161,188],"optimization.":[26],"This":[27,107,169],"paper":[28],"proposed":[29,131],"a":[30,172],"hybrid":[31,78],"artificial":[32,179],"intelligence":[33,180],"(AI)":[34],"model":[35,79,132],"output":[41],"in":[42,52,62],"designed":[51],"prior":[54],"research":[55],"dataset":[58],"primarily":[59],"comprises":[60,80],"meteorological":[61],"Lhasa,":[63],"Tibet,":[64],"China":[65],"operational":[67],"parameters":[68],"were":[70],"collected":[71],"per":[72],"hour,":[73],"totaling":[74],"4461":[75],"samples.":[76],"cross-correlation":[82],"Improved":[83],"Complete":[84],"Ensemble":[85],"Empirical":[86],"Mode":[87],"Decomposition":[88],"with":[89,123,141],"Adaptive":[90],"Noise":[91],"(CC-ICEEMDAN)":[92],"method":[93],"denoising,":[95],"Convolutional":[96],"Neural":[97],"Network":[98],"(CNN)":[99],"Gated":[101],"Recurrent":[102],"Unit":[103],"(GRU)":[104],"forecasting.":[106],"structure":[108],"captures":[109],"multi-dimensional":[110],"features":[111],"to":[112],"boost":[113],"accuracy":[115],"develop":[117],"learning":[118,128],"speed":[119],"robustness.":[121],"Compared":[122],"other":[124],"five":[125],"typical":[126],"deep":[127],"models,":[129],"most":[135],"suitable":[136],"PGF":[138],"MECPG":[140],"Mean":[142,148,155,163],"Squared":[143,164],"Error":[144,151,157,165],"(MSE)":[145],"0.0005,":[147],"Absolute":[149,156],"Percentage":[150],"(MAPE)":[152],"2.8,":[154],"(MAE)":[158],"0.014,":[160],"Root":[162],"(RMSE)":[166],"0.02.":[168],"study":[170],"provides":[171],"theoretical":[173],"basis":[174],"realization":[177],"application":[181],"involves":[183],"real-time":[185],"peak":[186],"optimal":[189],"allocation":[190],"MECPG.":[192]},"counts_by_year":[{"year":2026,"cited_by_count":10},{"year":2025,"cited_by_count":4}],"updated_date":"2026-07-22T07:51:19.307946","created_date":"2025-10-10T00:00:00"}