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Forecasting Building Electric Consumption Patterns through Statistical Methods

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Forecasting Building Electric Consumption Patterns through Statistical Methods

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dc.contributor.author Serrano-Guerrero, Xavier es_ES
dc.contributor.author Siavichay, Luis-Fernando es_ES
dc.contributor.author Clairand, Jean-Michel es_ES
dc.contributor.author Escrivá-Escrivá, Guillermo es_ES
dc.date.accessioned 2022-01-18T08:13:16Z
dc.date.available 2022-01-18T08:13:16Z
dc.date.issued 2020-01-01 es_ES
dc.identifier.isbn 978-3-030-32022-5 es_ES
dc.identifier.issn 2194-5365 es_ES
dc.identifier.uri http://hdl.handle.net/10251/179855
dc.description.abstract [EN] The electricity sector presents new challenges in the operation and planning of power systems, such as the forecast of power demand. This paper proposes a comprehensive approach for evaluating statistical methods and techniques of electric demand forecast. The proposed approach is based on smoothing methods, simple and multiple regressions, and ARIMA models, applied to two real university buildings from Ecuador and Spain. The results are analyzed by statistical metrics to assess their predictive capacity, and they indicate that the Holt-Winter and ARIMA methods have the best performance to forecast the electricity demand (ED). es_ES
dc.language Inglés es_ES
dc.publisher Springer Nature es_ES
dc.relation.ispartof Advances in Emerging Trends and Technologies es_ES
dc.relation.ispartofseries Advances in Intelligent Systems and Computing;1067 es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject ARIMA es_ES
dc.subject Electric demand es_ES
dc.subject Forecast es_ES
dc.subject Load uncertainties es_ES
dc.subject Statistical methods es_ES
dc.subject Winter es_ES
dc.subject.classification INGENIERIA ELECTRICA es_ES
dc.title Forecasting Building Electric Consumption Patterns through Statistical Methods es_ES
dc.type Comunicación en congreso es_ES
dc.type Artículo es_ES
dc.type Capítulo de libro es_ES
dc.identifier.doi 10.1007/978-3-030-32033-1_16 es_ES
dc.relation.projectID info:eu-repo/grantAgreement///6602277-01//Apoyo tecnológico entre la UPV y la Universidad Politécnica Salesiana para la mejora de la eficiencia energética en edificaciones de la zona ecuatorial de los Andes/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Ingeniería Eléctrica - Departament d'Enginyeria Elèctrica es_ES
dc.description.bibliographicCitation Serrano-Guerrero, X.; Siavichay, L.; Clairand, J.; Escrivá-Escrivá, G. (2020). Forecasting Building Electric Consumption Patterns through Statistical Methods. Springer Nature. 164-175. https://doi.org/10.1007/978-3-030-32033-1_16 es_ES
dc.description.accrualMethod S es_ES
dc.relation.conferencename 1er Congreso Internacional sobre Avances en Nuevas Tendencias y Tecnologías (ICAETT 2019) es_ES
dc.relation.conferencedate Mayo 29-31,2019 es_ES
dc.relation.conferenceplace Quito, Ecuador es_ES
dc.relation.publisherversion https://doi.org/10.1007/978-3-030-32033-1_16 es_ES
dc.description.upvformatpinicio 164 es_ES
dc.description.upvformatpfin 175 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.relation.pasarela S\406589 es_ES
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