{"id":"https://openalex.org/W4380302496","doi":"https://doi.org/10.1109/icaci58115.2023.10146172","title":"Improvement of Long Short-Term Memory via CEEMDAN and Logistic Maps for the Power Consumption Forecasting","display_name":"Improvement of Long Short-Term Memory via CEEMDAN and Logistic Maps for the Power Consumption Forecasting","publication_year":2023,"publication_date":"2023-05-06","ids":{"openalex":"https://openalex.org/W4380302496","doi":"https://doi.org/10.1109/icaci58115.2023.10146172"},"language":"en","primary_location":{"id":"doi:10.1109/icaci58115.2023.10146172","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/icaci58115.2023.10146172","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 15th International Conference on Advanced Computational Intelligence (ICACI)","raw_type":"proceedings-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/A5019520920","display_name":"Sarunyoo Boriratrit","orcid":"https://orcid.org/0000-0002-7463-8993"},"institutions":[{"id":"https://openalex.org/I179193067","display_name":"Khon Kaen University","ror":"https://ror.org/03cq4gr50","country_code":"TH","type":"education","lineage":["https://openalex.org/I179193067"]}],"countries":["TH"],"is_corresponding":false,"raw_author_name":"Sarunyoo Boriratrit","raw_affiliation_strings":["Faculty of Engineering Khon Kaen University,Department of Electrical Engineering,Khon Kaen,Thailand","Department of Electrical Engineering, Faculty of Engineering Khon Kaen University, Khon Kaen, Thailand"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty of Engineering Khon Kaen University,Department of Electrical Engineering,Khon Kaen,Thailand","institution_ids":["https://openalex.org/I179193067"]},{"raw_affiliation_string":"Department of Electrical Engineering, Faculty of Engineering Khon Kaen University, Khon Kaen, Thailand","institution_ids":["https://openalex.org/I179193067"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5036439921","display_name":"Rongrit Chatthaworn","orcid":"https://orcid.org/0000-0001-9258-7141"},"institutions":[{"id":"https://openalex.org/I179193067","display_name":"Khon Kaen University","ror":"https://ror.org/03cq4gr50","country_code":"TH","type":"education","lineage":["https://openalex.org/I179193067"]}],"countries":["TH"],"is_corresponding":false,"raw_author_name":"Rongrit Chatthaworn","raw_affiliation_strings":["Faculty of Engineering Khon Kaen University,Department of Electrical Engineering,Khon Kaen,Thailand","Department of Electrical Engineering, Faculty of Engineering Khon Kaen University, Khon Kaen, Thailand"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty of Engineering Khon Kaen University,Department of Electrical Engineering,Khon Kaen,Thailand","institution_ids":["https://openalex.org/I179193067"]},{"raw_affiliation_string":"Department of Electrical Engineering, Faculty of Engineering Khon Kaen University, Khon Kaen, Thailand","institution_ids":["https://openalex.org/I179193067"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.1227,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.4109643,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"8","issue":null,"first_page":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11052","display_name":"Energy Load and Power Forecasting","score":1.0,"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":1.0,"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":0.9955000281333923,"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"}},{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.9918000102043152,"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/overfitting","display_name":"Overfitting","score":0.8309555053710938},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7897934317588806},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6290391683578491},{"id":"https://openalex.org/keywords/outlier","display_name":"Outlier","score":0.6071475744247437},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5856647491455078},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.5363759994506836},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.4406232237815857},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3730211853981018},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.11812222003936768}],"concepts":[{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.8309555053710938},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7897934317588806},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6290391683578491},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.6071475744247437},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5856647491455078},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5363759994506836},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.4406232237815857},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3730211853981018},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.11812222003936768},{"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/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icaci58115.2023.10146172","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/icaci58115.2023.10146172","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 15th International Conference on Advanced Computational Intelligence (ICACI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.8399999737739563,"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320322335","display_name":"Khon Kaen University","ror":"https://ror.org/03cq4gr50"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W2064675550","https://openalex.org/W2125056386","https://openalex.org/W2942606504","https://openalex.org/W3024158773","https://openalex.org/W3090232294","https://openalex.org/W3191161296","https://openalex.org/W4200215659","https://openalex.org/W4210558189","https://openalex.org/W4229040435","https://openalex.org/W4240061960","https://openalex.org/W4288901802","https://openalex.org/W4311853407","https://openalex.org/W4313315958","https://openalex.org/W4321503048","https://openalex.org/W6762836260"],"related_works":["https://openalex.org/W4362597605","https://openalex.org/W1574414179","https://openalex.org/W4297676672","https://openalex.org/W3009056573","https://openalex.org/W2922073769","https://openalex.org/W4281702477","https://openalex.org/W2490526372","https://openalex.org/W2989932438","https://openalex.org/W4387297750","https://openalex.org/W2186333919"],"abstract_inverted_index":{"Nowadays,":[0],"machine":[1,34,45],"learning":[2,35,46],"is":[3,20,61],"an":[4,38,161],"essential":[5,39],"factor":[6],"in":[7,16,64,84],"computational":[8],"intelligence":[9],"that":[10,24,85,155],"can":[11,81,87,105,159],"provide":[12,160],"results":[13,153],"and":[14,33,51,68,98,133],"solutions":[15],"many":[17,65,96],"cases.":[18],"Forecasting":[19],"a":[21,123],"crucial":[22],"case":[23],"uses":[25],"historical":[26],"data":[27,31,174],"to":[28,74,107,113,166,170],"predict":[29],"future":[30],"trends,":[32],"has":[36],"become":[37],"model":[40,60,158],"for":[41],"predictive":[42],"methods":[43],"because":[44],"provides":[47],"high":[48],"forecast":[49],"accuracy":[50],"reliable":[52],"result.":[53],"The":[54,151],"fascinating":[55],"Long":[56],"Short-Term":[57],"Memory":[58],"(LSTM)":[59],"widely":[62],"used":[63],"forecasting":[66,116],"cases":[67],"gives":[69],"exceptional":[70],"results.":[71],"However,":[72],"according":[73],"various":[75],"studies,":[76],"the":[77,89,93,99,108,115,135,139,156,171,176],"issues":[78],"of":[79,101,118,164],"LSTM":[80,86,102,125],"be":[82],"addressed":[83],"cause":[88],"overfitting":[90],"phenomenon":[91],"when":[92,168],"dataset":[94,137],"contains":[95],"noises,":[97],"randomization":[100],"input":[103],"weight":[104],"occur":[106],"outlier":[109],"sensitivity.":[110],"In":[111],"order":[112],"improve":[114],"performance":[117],"LSTM,":[119],"this":[120],"paper":[121],"proposes":[122],"novel":[124],"method":[126],"by":[127],"optimizing":[128],"with":[129,138,145],"Logistic":[130],"Maps":[131],"(LM)":[132],"handling":[134],"import":[136],"Complete":[140],"Ensemble":[141],"Empirical":[142],"Mode":[143],"Decomposition":[144],"Adaptive":[146],"Noise":[147],"(CEEMDAN),":[148],"namely,":[149],"CEEMDAN-LM-LSTM.":[150],"experimental":[152],"show":[154],"proposed":[157],"r-squared":[162],"value":[163],"up":[165],"0.9999":[167],"applied":[169],"power":[172],"consumption":[173],"from":[175],"Tetouan,":[177],"Morocco":[178],"dataset.":[179]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
