Forecasting Electric Load Demand through Advanced Statistical Techniques

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Traditional forecasting models have been widely used for decision-making in production, finance and energy. Such is the case of the ARIMA models, developed in the 1970s by George Box and Gwilym Jenkins [1], which incorporate characteristics of the past models of the same series, according to their a...

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Detalles Bibliográficos
Autores: Silva, Jesús, Senior Naveda, Alexa, García Guliany, Jesús, Niebles Núẽz, William, Hernández Palma, Hugo
Formato: artículo
Fecha de Publicación:2020
Institución:Universidad Peruana de Ciencias Aplicadas
Repositorio:UPC-Institucional
Lenguaje:inglés
OAI Identifier:oai:repositorioacademico.upc.edu.pe:10757/652142
Enlace del recurso:http://hdl.handle.net/10757/652142
Nivel de acceso:acceso abierto
Materia:Bayesian networks
Decision making
Bayesian methods
Colombia
Electric load demands
Forecasting models
Statistical techniques
Electric load forecasting
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dc.title.en_US.fl_str_mv Forecasting Electric Load Demand through Advanced Statistical Techniques
title Forecasting Electric Load Demand through Advanced Statistical Techniques
spellingShingle Forecasting Electric Load Demand through Advanced Statistical Techniques
Silva, Jesús
Bayesian networks
Decision making
Bayesian methods
Colombia
Electric load demands
Forecasting models
Statistical techniques
Electric load forecasting
title_short Forecasting Electric Load Demand through Advanced Statistical Techniques
title_full Forecasting Electric Load Demand through Advanced Statistical Techniques
title_fullStr Forecasting Electric Load Demand through Advanced Statistical Techniques
title_full_unstemmed Forecasting Electric Load Demand through Advanced Statistical Techniques
title_sort Forecasting Electric Load Demand through Advanced Statistical Techniques
author Silva, Jesús
author_facet Silva, Jesús
Senior Naveda, Alexa
García Guliany, Jesús
Niebles Núẽz, William
Hernández Palma, Hugo
author_role author
author2 Senior Naveda, Alexa
García Guliany, Jesús
Niebles Núẽz, William
Hernández Palma, Hugo
author2_role author
author
author
author
dc.contributor.author.fl_str_mv Silva, Jesús
Senior Naveda, Alexa
García Guliany, Jesús
Niebles Núẽz, William
Hernández Palma, Hugo
dc.subject.en_US.fl_str_mv Bayesian networks
Decision making
Bayesian methods
Colombia
Electric load demands
Forecasting models
Statistical techniques
Electric load forecasting
topic Bayesian networks
Decision making
Bayesian methods
Colombia
Electric load demands
Forecasting models
Statistical techniques
Electric load forecasting
description Traditional forecasting models have been widely used for decision-making in production, finance and energy. Such is the case of the ARIMA models, developed in the 1970s by George Box and Gwilym Jenkins [1], which incorporate characteristics of the past models of the same series, according to their autocorrelation. This work compares advanced statistical methods for determining the demand for electricity in Colombia, including the SARIMA, econometric and Bayesian methods.
publishDate 2020
dc.date.accessioned.none.fl_str_mv 2020-07-02T16:24:49Z
dc.date.available.none.fl_str_mv 2020-07-02T16:24:49Z
dc.date.issued.fl_str_mv 2020-01-07
dc.type.en_US.fl_str_mv info:eu-repo/semantics/article
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dc.identifier.issn.none.fl_str_mv 17426588
dc.identifier.doi.none.fl_str_mv 10.1088/1742-6596/1432/1/012031
dc.identifier.uri.none.fl_str_mv http://hdl.handle.net/10757/652142
dc.identifier.eissn.none.fl_str_mv 17426596
dc.identifier.journal.en_US.fl_str_mv Journal of Physics: Conference Series
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dc.language.iso.en_US.fl_str_mv eng
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Forecasting Electric Load Demand through Advanced Statistical Techniques
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