{"id":"https://openalex.org/W7154496299","doi":"https://doi.org/10.1016/j.engappai.2026.114804","title":"A convolutional neural network-long short-term memory hybrid deep learning model for predicting uncontrollable energy consumption balance in France","display_name":"A convolutional neural network-long short-term memory hybrid deep learning model for predicting uncontrollable energy consumption balance in France","publication_year":2026,"publication_date":"2026-04-15","ids":{"openalex":"https://openalex.org/W7154496299","doi":"https://doi.org/10.1016/j.engappai.2026.114804"},"language":"en","primary_location":{"id":"doi:10.1016/j.engappai.2026.114804","is_oa":false,"landing_page_url":"https://doi.org/10.1016/j.engappai.2026.114804","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":null,"license_id":null,"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":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5032283943","display_name":"Guangying Jin","orcid":"https://orcid.org/0000-0002-4459-8425"},"institutions":[{"id":"https://openalex.org/I43313876","display_name":"Dalian Maritime University","ror":"https://ror.org/002b7nr53","country_code":"CN","type":"education","lineage":["https://openalex.org/I43313876"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Guangying Jin","raw_affiliation_strings":["School of Maritime Economics and Management, Dalian Maritime University, Dalian, 116026, China"],"raw_orcid":"https://orcid.org/0000-0002-4459-8425","affiliations":[{"raw_affiliation_string":"School of Maritime Economics and Management, Dalian Maritime University, Dalian, 116026, China","institution_ids":["https://openalex.org/I43313876"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101372177","display_name":"Jianyu Zhao","orcid":"https://orcid.org/0009-0000-9812-2046"},"institutions":[{"id":"https://openalex.org/I43313876","display_name":"Dalian Maritime University","ror":"https://ror.org/002b7nr53","country_code":"CN","type":"education","lineage":["https://openalex.org/I43313876"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jianyu Zhao","raw_affiliation_strings":["School of Maritime Economics and Management, Dalian Maritime University, Dalian, 116026, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Maritime Economics and Management, Dalian Maritime University, Dalian, 116026, China","institution_ids":["https://openalex.org/I43313876"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5032283943"],"corresponding_institution_ids":["https://openalex.org/I43313876"],"apc_list":{"value":3170,"currency":"USD","value_usd":3170},"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.38554573,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"176","issue":null,"first_page":"114804","last_page":"114804"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11052","display_name":"Energy Load and Power Forecasting","score":0.743399977684021,"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":0.743399977684021,"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/T11185","display_name":"Integrated Energy Systems Optimization","score":0.16089999675750732,"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/T11276","display_name":"Solar Radiation and Photovoltaics","score":0.021900000050663948,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6600000262260437},{"id":"https://openalex.org/keywords/feature-engineering","display_name":"Feature engineering","score":0.5891000032424927},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5740000009536743},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5555999875068665},{"id":"https://openalex.org/keywords/energy-consumption","display_name":"Energy consumption","score":0.5198000073432922},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.48669999837875366},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.42399999499320984},{"id":"https://openalex.org/keywords/mean-absolute-percentage-error","display_name":"Mean absolute percentage error","score":0.4235000014305115},{"id":"https://openalex.org/keywords/quantile","display_name":"Quantile","score":0.41499999165534973}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8610000014305115},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6600000262260437},{"id":"https://openalex.org/C2778827112","wikidata":"https://www.wikidata.org/wiki/Q22245680","display_name":"Feature engineering","level":3,"score":0.5891000032424927},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5740000009536743},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5555999875068665},{"id":"https://openalex.org/C2780165032","wikidata":"https://www.wikidata.org/wiki/Q16869822","display_name":"Energy consumption","level":2,"score":0.5198000073432922},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5073000192642212},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.48669999837875366},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.42399999499320984},{"id":"https://openalex.org/C150217764","wikidata":"https://www.wikidata.org/wiki/Q6803607","display_name":"Mean absolute percentage error","level":3,"score":0.4235000014305115},{"id":"https://openalex.org/C118671147","wikidata":"https://www.wikidata.org/wiki/Q578714","display_name":"Quantile","level":2,"score":0.41499999165534973},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.38420000672340393},{"id":"https://openalex.org/C187691185","wikidata":"https://www.wikidata.org/wiki/Q2020720","display_name":"Grid","level":2,"score":0.3840000033378601},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3792000114917755},{"id":"https://openalex.org/C188573790","wikidata":"https://www.wikidata.org/wiki/Q12705","display_name":"Renewable energy","level":2,"score":0.3569999933242798},{"id":"https://openalex.org/C179518139","wikidata":"https://www.wikidata.org/wiki/Q5140297","display_name":"Coding (social sciences)","level":2,"score":0.3393999934196472},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.328900009393692},{"id":"https://openalex.org/C2742236","wikidata":"https://www.wikidata.org/wiki/Q924713","display_name":"Efficient energy use","level":2,"score":0.321399986743927},{"id":"https://openalex.org/C2777423268","wikidata":"https://www.wikidata.org/wiki/Q3663116","display_name":"Energy balance","level":2,"score":0.31450000405311584},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.31119999289512634},{"id":"https://openalex.org/C78600449","wikidata":"https://www.wikidata.org/wiki/Q43302","display_name":"Wind power","level":2,"score":0.305400013923645},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.30149999260902405},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2976999878883362},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.25870001316070557},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.25609999895095825},{"id":"https://openalex.org/C122383733","wikidata":"https://www.wikidata.org/wiki/Q865920","display_name":"Approximation error","level":2,"score":0.2547000050544739},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.25360000133514404},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.2529999911785126},{"id":"https://openalex.org/C2779990667","wikidata":"https://www.wikidata.org/wiki/Q5953266","display_name":"Hybrid neural network","level":3,"score":0.250900000333786}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1016/j.engappai.2026.114804","is_oa":false,"landing_page_url":"https://doi.org/10.1016/j.engappai.2026.114804","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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Engineering Applications of Artificial Intelligence","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":52,"referenced_works":["https://openalex.org/W1995384696","https://openalex.org/W1997225816","https://openalex.org/W2064675550","https://openalex.org/W2604482548","https://openalex.org/W2775155156","https://openalex.org/W2944900364","https://openalex.org/W2948490758","https://openalex.org/W2989672662","https://openalex.org/W3000399655","https://openalex.org/W3006082917","https://openalex.org/W3006198287","https://openalex.org/W3115103108","https://openalex.org/W3118396091","https://openalex.org/W3129054759","https://openalex.org/W3132882148","https://openalex.org/W3142394151","https://openalex.org/W3149578452","https://openalex.org/W3159165024","https://openalex.org/W3175155068","https://openalex.org/W3178756396","https://openalex.org/W3179483397","https://openalex.org/W3204788309","https://openalex.org/W3213761405","https://openalex.org/W4206023940","https://openalex.org/W4210866578","https://openalex.org/W4213021104","https://openalex.org/W4214892038","https://openalex.org/W4220801201","https://openalex.org/W4220954082","https://openalex.org/W4281899822","https://openalex.org/W4290052851","https://openalex.org/W4296220030","https://openalex.org/W4297499594","https://openalex.org/W4309676689","https://openalex.org/W4310857502","https://openalex.org/W4318023038","https://openalex.org/W4321485251","https://openalex.org/W4368228252","https://openalex.org/W4378894778","https://openalex.org/W4382655263","https://openalex.org/W4387479671","https://openalex.org/W4390109037","https://openalex.org/W4390569940","https://openalex.org/W4390660405","https://openalex.org/W4391155129","https://openalex.org/W4393216334","https://openalex.org/W4395697085","https://openalex.org/W4400721553","https://openalex.org/W4403243966","https://openalex.org/W4405552907","https://openalex.org/W4409195383","https://openalex.org/W4414714594"],"related_works":[],"abstract_inverted_index":null,"counts_by_year":[],"updated_date":"2026-06-15T08:34:33.830935","created_date":"2026-04-16T00:00:00"}