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dc.contributor.authorCriado Ramón, David
dc.contributor.authorBaca Ruiz, Luis Gonzaga 
dc.contributor.authorPegalajar Jiménez, María Del Carmen 
dc.date.accessioned2023-07-26T08:36:28Z
dc.date.available2023-07-26T08:36:28Z
dc.date.issued2023-05-10
dc.identifier.citationCriado-Ramón, D.; Ruiz, L.G.B.; Pegalajar, M.C. An Improved Pattern Sequence-Based Energy Load Forecast Algorithm Based on Self-Organizing Maps and Artificial Neural Networks. Big Data Cogn. Comput. 2023, 7, 92. [https://doi.org/10.3390/bdcc7020092]es_ES
dc.identifier.urihttps://hdl.handle.net/10481/84007
dc.description.abstractPattern sequence-based models are a type of forecasting algorithm that utilizes clustering and other techniques to produce easily interpretable predictions faster than traditional machine learning models. This research focuses on their application in energy demand forecasting and introduces two significant contributions to the field. Firstly, this study evaluates the use of pattern sequence-based models with large datasets. Unlike previous works that use only one dataset or multiple datasets with less than two years of data, this work evaluates the models in three different public datasets, each containing eleven years of data. Secondly, we propose a new pattern sequencebased algorithm that uses a genetic algorithm to optimize the number of clusters alongside all other hyperparameters of the forecasting method, instead of using the Cluster Validity Indices (CVIs) commonly used in previous proposals. The results indicate that neural networks provide more accurate results than any pattern sequence-based algorithm and that our proposed algorithm outperforms other pattern sequence-based algorithms, albeit with a longer training time.es_ES
dc.description.sponsorshipI+D+i FEDER 2020 project B-TIC-42-UGR20 “Consejería de Universidad, Investigación e Innovación de la Junta de Andalucíaes_ES
dc.description.sponsorshipMinisterio de Ciencia e Innovación” (Spain) (Grant PID2020-112495RB-C21 funded by MCIN/ AEI /10.13039/501100011033es_ES
dc.language.isoenges_ES
dc.publisherMDPIes_ES
dc.rightsAtribución 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectTime series forecastinges_ES
dc.subjectClusteringes_ES
dc.subjectPatternes_ES
dc.subjectGenetic algorithmes_ES
dc.subjectEnergyes_ES
dc.titleAn Improved Pattern Sequence-Based Energy Load Forecast Algorithm Based on Self-Organizing Maps and Artificial Neural Networkses_ES
dc.typejournal articlees_ES
dc.rights.accessRightsopen accesses_ES
dc.identifier.doi10.3390/bdcc7020092
dc.type.hasVersionVoRes_ES


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