{"id":"https://openalex.org/W7126429681","doi":"https://doi.org/10.3390/e28020158","title":"Ensemble Entropy with Adaptive Deep Fusion for Short-Term Power Load Forecasting","display_name":"Ensemble Entropy with Adaptive Deep Fusion for Short-Term Power Load Forecasting","publication_year":2026,"publication_date":"2026-01-31","ids":{"openalex":"https://openalex.org/W7126429681","doi":"https://doi.org/10.3390/e28020158","pmid":"https://pubmed.ncbi.nlm.nih.gov/41751662"},"language":"en","primary_location":{"id":"doi:10.3390/e28020158","is_oa":true,"landing_page_url":"https://doi.org/10.3390/e28020158","pdf_url":null,"source":{"id":"https://openalex.org/S195231649","display_name":"Entropy","issn_l":"1099-4300","issn":["1099-4300"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Entropy","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj","pubmed"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.3390/e28020158","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5124554366","display_name":"Yiling Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I9086337","display_name":"Taiyuan University of Technology","ror":"https://ror.org/03kv08d37","country_code":"CN","type":"education","lineage":["https://openalex.org/I9086337"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yiling Wang","raw_affiliation_strings":["College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, China","institution_ids":["https://openalex.org/I9086337"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035048904","display_name":"Yan Niu","orcid":"https://orcid.org/0000-0002-8136-6631"},"institutions":[{"id":"https://openalex.org/I9086337","display_name":"Taiyuan University of Technology","ror":"https://ror.org/03kv08d37","country_code":"CN","type":"education","lineage":["https://openalex.org/I9086337"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yan Niu","raw_affiliation_strings":["College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, China"],"raw_orcid":"https://orcid.org/0000-0002-8136-6631","affiliations":[{"raw_affiliation_string":"College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, China","institution_ids":["https://openalex.org/I9086337"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079650751","display_name":"X LI","orcid":null},"institutions":[{"id":"https://openalex.org/I4210155468","display_name":"ZTT (China)","ror":"https://ror.org/056yydt13","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210155468"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xuejun Li","raw_affiliation_strings":["Jiangsu Haohan Information Technology Co., Ltd., Nantong 226300, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Jiangsu Haohan Information Technology Co., Ltd., Nantong 226300, China","institution_ids":["https://openalex.org/I4210155468"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124592804","display_name":"Xianglong Dai","orcid":null},"institutions":[{"id":"https://openalex.org/I4210155468","display_name":"ZTT (China)","ror":"https://ror.org/056yydt13","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210155468"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xianglong Dai","raw_affiliation_strings":["Jiangsu Haohan Information Technology Co., Ltd., Nantong 226300, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Jiangsu Haohan Information Technology Co., Ltd., Nantong 226300, China","institution_ids":["https://openalex.org/I4210155468"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089691773","display_name":"Xiaopeng Wang","orcid":"https://orcid.org/0009-0007-1583-7793"},"institutions":[{"id":"https://openalex.org/I4210155468","display_name":"ZTT (China)","ror":"https://ror.org/056yydt13","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210155468"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaopeng Wang","raw_affiliation_strings":["Jiangsu Haohan Information Technology Co., Ltd., Nantong 226300, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Jiangsu Haohan Information Technology Co., Ltd., Nantong 226300, China","institution_ids":["https://openalex.org/I4210155468"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124683515","display_name":"Yong Jiang","orcid":null},"institutions":[{"id":"https://openalex.org/I4210155468","display_name":"ZTT (China)","ror":"https://ror.org/056yydt13","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210155468"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yong Jiang","raw_affiliation_strings":["Jiangsu Haohan Information Technology Co., Ltd., Nantong 226300, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Jiangsu Haohan Information Technology Co., Ltd., Nantong 226300, China","institution_ids":["https://openalex.org/I4210155468"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5046895505","display_name":"Chenghu He","orcid":null},"institutions":[{"id":"https://openalex.org/I4210155468","display_name":"ZTT (China)","ror":"https://ror.org/056yydt13","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210155468"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chenghu He","raw_affiliation_strings":["Jiangsu Haohan Information Technology Co., Ltd., Nantong 226300, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Jiangsu Haohan Information Technology Co., Ltd., Nantong 226300, China","institution_ids":["https://openalex.org/I4210155468"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5124557992","display_name":"Li Zhou","orcid":null},"institutions":[{"id":"https://openalex.org/I9086337","display_name":"Taiyuan University of Technology","ror":"https://ror.org/03kv08d37","country_code":"CN","type":"education","lineage":["https://openalex.org/I9086337"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Li Zhou","raw_affiliation_strings":["College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, China","institution_ids":["https://openalex.org/I9086337"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":8,"corresponding_author_ids":["https://openalex.org/A5124557992"],"corresponding_institution_ids":["https://openalex.org/I9086337"],"apc_list":{"value":2000,"currency":"CHF","value_usd":2165},"apc_paid":{"value":2000,"currency":"CHF","value_usd":2165},"fwci":8.7515,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.95145643,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":95,"max":98},"biblio":{"volume":"28","issue":"2","first_page":"158","last_page":"158"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11052","display_name":"Energy Load and Power Forecasting","score":0.9036999940872192,"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":0.9036999940872192,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.01080000028014183,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.008500000461935997,"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/entropy","display_name":"Entropy (arrow of time)","score":0.5878000259399414},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.4659000039100647},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4002000093460083},{"id":"https://openalex.org/keywords/ensemble-forecasting","display_name":"Ensemble forecasting","score":0.3882000148296356},{"id":"https://openalex.org/keywords/electric-power-system","display_name":"Electric power system","score":0.35929998755455017},{"id":"https://openalex.org/keywords/fusion","display_name":"Fusion","score":0.35659998655319214},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3276999890804291}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7217000126838684},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.5878000259399414},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5465999841690063},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.4659000039100647},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.41499999165534973},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4002000093460083},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39480000734329224},{"id":"https://openalex.org/C119898033","wikidata":"https://www.wikidata.org/wiki/Q3433888","display_name":"Ensemble forecasting","level":2,"score":0.3882000148296356},{"id":"https://openalex.org/C89227174","wikidata":"https://www.wikidata.org/wiki/Q2388981","display_name":"Electric power system","level":3,"score":0.35929998755455017},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.35659998655319214},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3276999890804291},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.32010000944137573},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.3190000057220459},{"id":"https://openalex.org/C171752962","wikidata":"https://www.wikidata.org/wiki/Q255166","display_name":"Kullback\u2013Leibler divergence","level":2,"score":0.3127000033855438},{"id":"https://openalex.org/C167981619","wikidata":"https://www.wikidata.org/wiki/Q1685498","display_name":"Cross entropy","level":3,"score":0.3107999861240387},{"id":"https://openalex.org/C66696666","wikidata":"https://www.wikidata.org/wiki/Q17105612","display_name":"Sample entropy","level":3,"score":0.30880001187324524},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.30329999327659607},{"id":"https://openalex.org/C101721835","wikidata":"https://www.wikidata.org/wiki/Q813908","display_name":"Conditional entropy","level":3,"score":0.28290000557899475},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2567000091075897}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.3390/e28020158","is_oa":true,"landing_page_url":"https://doi.org/10.3390/e28020158","pdf_url":null,"source":{"id":"https://openalex.org/S195231649","display_name":"Entropy","issn_l":"1099-4300","issn":["1099-4300"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Entropy","raw_type":"journal-article"},{"id":"pmid:41751662","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/41751662","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Entropy (Basel, Switzerland)","raw_type":null},{"id":"pmh:oai:doaj.org/article:6f2c8ff1aeb74025be4ca0b66d7feeda","is_oa":false,"landing_page_url":"https://doaj.org/article/6f2c8ff1aeb74025be4ca0b66d7feeda","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Entropy, Vol 28, Iss 2, p 158 (2026)","raw_type":"article"},{"id":"pmh:oai:pubmedcentral.nih.gov:12938898","is_oa":true,"landing_page_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC12938898/","pdf_url":null,"source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Entropy (Basel)","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/e28020158","is_oa":true,"landing_page_url":"https://doi.org/10.3390/e28020158","pdf_url":null,"source":{"id":"https://openalex.org/S195231649","display_name":"Entropy","issn_l":"1099-4300","issn":["1099-4300"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Entropy","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Accurate":[0],"power":[1,14,24,58,211],"load":[2,25,59,212],"forecasting":[3,110,213],"is":[4,99,119,143,159],"crucial":[5],"for":[6,30,210],"ensuring":[7],"the":[8,17,186,207,216,246,251,255],"safety":[9],"and":[10,20,39,72,79,105,168,201,226,228,240,254],"economic":[11],"operation":[12],"of":[13,23,91,196,199,203,249],"systems.":[15],"However,":[16],"complex,":[18],"non-stationary,":[19],"heterogeneous":[21],"nature":[22],"data":[26],"presents":[27],"significant":[28],"challenges":[29],"traditional":[31,229],"prediction":[32],"methods,":[33],"particularly":[34],"in":[35,82,174,189],"capturing":[36],"instantaneous":[37,66],"dynamics":[38],"effectively":[40],"fusing":[41],"multi-feature":[42,57],"information.":[43],"This":[44],"paper":[45],"proposes":[46],"a":[47,88,179],"novel":[48],"framework\u2014Ensemble":[49],"Entropy":[50],"with":[51,112,127],"Adaptive":[52],"Deep":[53],"Fusion":[54],"(EEADF)\u2014for":[55],"short-term":[56],"forecasting.":[60],"The":[61],"framework":[62],"introduces":[63],"an":[64],"ensemble":[65],"entropy":[67,75,129,170,230,252],"extraction":[68],"module":[69],"to":[70,101],"compute":[71],"fuse":[73],"multiple":[74],"types":[76],"(approximate,":[77],"sample,":[78],"permutation":[80],"entropies)":[81],"real-time":[83],"within":[84],"sliding":[85],"windows,":[86],"creating":[87],"sensitive":[89],"representation":[90],"system":[92],"states.":[93],"A":[94,154],"task-adaptive":[95],"hierarchical":[96],"fusion":[97,118,142,256],"mechanism":[98],"employed":[100],"balance":[102],"computational":[103],"efficiency":[104],"model":[106,158],"expressivity.":[107],"For":[108,131],"time-series":[109],"tasks":[111,135],"relatively":[113],"structured":[114],"patterns,":[115,193],"feature":[116,148],"concatenation":[117],"used":[120],"that":[121,145,161,215],"directly":[122],"combines":[123],"LSTM":[124],"sequence":[125],"features":[126,171,253],"multimodal":[128,133,183],"features.":[130],"complex":[132],"understanding":[134],"requiring":[136],"nuanced":[137],"cross-modal":[138],"interactions,":[139],"multi-head":[140],"self-attention":[141],"implemented":[144],"dynamically":[146],"weights":[147],"importance":[149],"based":[150],"on":[151,178,206],"contextual":[152],"relevance.":[153],"dual-branch":[155],"deep":[156],"learning":[157],"constructed":[160],"processes":[162],"both":[163,250],"raw":[164],"sequences":[165],"(via":[166,172],"LSTM)":[167],"extracted":[169],"MLP)":[173],"parallel.":[175],"Extensive":[176],"experiments":[177],"carefully":[180],"designed":[181],"simulated":[182],"dataset":[184],"demonstrate":[185],"framework\u2019s":[187],"robustness":[188],"recognizing":[190],"diverse":[191],"dynamic":[192],"achieving":[194],"MSE":[195],"0.0125,":[197],"MAE":[198],"0.0794,":[200],"R2":[202],"0.9932.":[204],"Validation":[205],"real-world":[208],"ETDataset":[209],"confirms":[214],"proposed":[217],"method":[218],"significantly":[219],"outperforms":[220],"baseline":[221],"models":[222],"(LSTM,":[223],"TCN,":[224],"transformer,":[225],"informer)":[227],"methods":[231],"across":[232],"standard":[233],"evaluation":[234],"metrics":[235],"(MSE,":[236],"MAE,":[237],"RMSE,":[238],"MAPE,":[239],"R2).":[241],"Ablation":[242],"studies":[243],"further":[244],"verify":[245],"critical":[247],"roles":[248],"mechanism.":[257]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2026-02-02T00:00:00"}
