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Analyzing and predicting power consumption profiles using big data
dc.contributor.author | amelec, viloria | spa |
dc.contributor.author | Prieto Pulido, Ronald Antonio | spa |
dc.contributor.author | García Guiliany, Jesús | spa |
dc.contributor.author | Martínez Ventura, Jairo | spa |
dc.contributor.author | Hernández Palma, Hugo | spa |
dc.contributor.author | Jinete Torres, José | spa |
dc.contributor.author | REDONDO BILBAO, OSMAN ENRIQUE | spa |
dc.contributor.author | Pineda Lezam, Omar Bonerge | spa |
dc.date.accessioned | 2020-01-15T19:30:28Z | |
dc.date.available | 2020-01-15T19:30:28Z | |
dc.date.issued | 2019 | |
dc.identifier.issn | 18650929 | spa |
dc.identifier.uri | http://hdl.handle.net/11323/5828 | spa |
dc.description.abstract | The Euclidean distance (ED), the mean absolute error (MAE), the mean absolute percentage error (MAPE) and the root of the mean quadratic error (RMQE) are used to evaluate the predictive capability of the models supported by each statistical method, asserting, according to the assessment, that the best predictions come from the ARIMA method. This paper presents a prediction study for two buildings located at the University of Mumbai in India, in order to determine a method that fits the forecasts of organization expenses | spa |
dc.description.abstract | La distancia euclidiana (DE), el error absoluto medio (MAE), el error porcentual absoluto medio (MAPE) y la raíz del error cuadrático medio (RMQE) se utilizan para evaluar la capacidad predictiva de los modelos soportados por cada método estadístico, afirmando, según la evaluación, que las mejores predicciones provienen del método ARIMA. Este documento presenta un estudio de predicción para dos edificios ubicados en la Universidad de Mumbai en India, con el fin de determinar un método que se ajuste a las previsiones de gastos de la organización. | spa |
dc.description.abstract | Pérez Arriaga, J.I., Sánchez de Tembleque, L.J., Pardo, M.: La gestión de la demanda de electricidad, vol. I, no. I (2005) | spa |
dc.language.iso | eng | |
dc.publisher | Communications in Computer and Information Science | spa |
dc.relation.ispartof | https://doi.org/10.1007/978-981-15-1304-6_31 | spa |
dc.rights | CC0 1.0 Universal | spa |
dc.rights.uri | http://creativecommons.org/publicdomain/zero/1.0/ | spa |
dc.subject | Prediction | spa |
dc.subject | Power consumption | spa |
dc.subject | Big Data | spa |
dc.subject | ARIMA | spa |
dc.subject | Predicción | spa |
dc.subject | Consumo de energía | spa |
dc.title | Analyzing and predicting power consumption profiles using big data | spa |
dc.type | Artículo de revista | spa |
dc.rights.accessrights | info:eu-repo/semantics/openAccess | spa |
dc.identifier.instname | Corporación Universidad de la Costa | spa |
dc.identifier.reponame | REDICUC - Repositorio CUC | spa |
dc.identifier.repourl | https://repositorio.cuc.edu.co/ | spa |
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dc.title.translated | Análisis y predicción de perfiles de consumo de energía utilizando big data | spa |
dc.type.coar | http://purl.org/coar/resource_type/c_6501 | spa |
dc.type.content | Text | spa |
dc.type.driver | info:eu-repo/semantics/article | spa |
dc.type.redcol | http://purl.org/redcol/resource_type/ART | spa |
dc.type.version | info:eu-repo/semantics/acceptedVersion | spa |
dc.type.coarversion | http://purl.org/coar/version/c_ab4af688f83e57aa | spa |
dc.rights.coar | http://purl.org/coar/access_right/c_abf2 | spa |
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