Univariate versus Multivariate Models for Short-term Electricity Load Forecasting

Autores
Neto, Guilherme G.; Defilippo, Samuel B.; Hippert, Henrique S.
Año de publicación
2015
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
Online short-term load forecasts are needed for efficient demand management on power systems. To model the load, univariate and multivariate forecast approaches were developed: while the first consider the load as a linear function of its time series, the other also takes in account the nonlinear effects of weather-related variables (mainly the air temperature). Despite the wide recent literature on multivariate models, some authors state that univariate ones are sufficient for short-term purposes, claiming that including temperature variables unnecessarily elevates the model complexity, putting parsimony and robustness at risk. In this study, we compare the forecasts produced, for real data, by several univariate and multivariate time series and neural network-based load curve models. We then use a nonparametric hypothesis test to compare the daily mean errors of the best forecaster of each kind and, so, verify if considering the air temperature leads to any statistically significant improvement in the forecasting.
Sociedad Argentina de Informática e Investigación Operativa (SADIO)
Materia
Ciencias Informáticas
Neural nets
short-term load forecasting
load curve models
exponential smoothing
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by/3.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/59432

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spelling Univariate versus Multivariate Models for Short-term Electricity Load ForecastingNeto, Guilherme G.Defilippo, Samuel B.Hippert, Henrique S.Ciencias InformáticasNeural netsshort-term load forecastingload curve modelsexponential smoothingOnline short-term load forecasts are needed for efficient demand management on power systems. To model the load, univariate and multivariate forecast approaches were developed: while the first consider the load as a linear function of its time series, the other also takes in account the nonlinear effects of weather-related variables (mainly the air temperature). Despite the wide recent literature on multivariate models, some authors state that univariate ones are sufficient for short-term purposes, claiming that including temperature variables unnecessarily elevates the model complexity, putting parsimony and robustness at risk. In this study, we compare the forecasts produced, for real data, by several univariate and multivariate time series and neural network-based load curve models. We then use a nonparametric hypothesis test to compare the daily mean errors of the best forecaster of each kind and, so, verify if considering the air temperature leads to any statistically significant improvement in the forecasting.Sociedad Argentina de Informática e Investigación Operativa (SADIO)2015-09info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf143-151http://sedici.unlp.edu.ar/handle/10915/59432enginfo:eu-repo/semantics/altIdentifier/url/http://44jaiio.sadio.org.ar/sites/default/files/sio143-151.pdfinfo:eu-repo/semantics/altIdentifier/issn/2451-7550info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/3.0/Creative Commons Attribution 3.0 Unported (CC BY 3.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T11:07:01Zoai:sedici.unlp.edu.ar:10915/59432Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 11:07:02.005SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Univariate versus Multivariate Models for Short-term Electricity Load Forecasting
title Univariate versus Multivariate Models for Short-term Electricity Load Forecasting
spellingShingle Univariate versus Multivariate Models for Short-term Electricity Load Forecasting
Neto, Guilherme G.
Ciencias Informáticas
Neural nets
short-term load forecasting
load curve models
exponential smoothing
title_short Univariate versus Multivariate Models for Short-term Electricity Load Forecasting
title_full Univariate versus Multivariate Models for Short-term Electricity Load Forecasting
title_fullStr Univariate versus Multivariate Models for Short-term Electricity Load Forecasting
title_full_unstemmed Univariate versus Multivariate Models for Short-term Electricity Load Forecasting
title_sort Univariate versus Multivariate Models for Short-term Electricity Load Forecasting
dc.creator.none.fl_str_mv Neto, Guilherme G.
Defilippo, Samuel B.
Hippert, Henrique S.
author Neto, Guilherme G.
author_facet Neto, Guilherme G.
Defilippo, Samuel B.
Hippert, Henrique S.
author_role author
author2 Defilippo, Samuel B.
Hippert, Henrique S.
author2_role author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
Neural nets
short-term load forecasting
load curve models
exponential smoothing
topic Ciencias Informáticas
Neural nets
short-term load forecasting
load curve models
exponential smoothing
dc.description.none.fl_txt_mv Online short-term load forecasts are needed for efficient demand management on power systems. To model the load, univariate and multivariate forecast approaches were developed: while the first consider the load as a linear function of its time series, the other also takes in account the nonlinear effects of weather-related variables (mainly the air temperature). Despite the wide recent literature on multivariate models, some authors state that univariate ones are sufficient for short-term purposes, claiming that including temperature variables unnecessarily elevates the model complexity, putting parsimony and robustness at risk. In this study, we compare the forecasts produced, for real data, by several univariate and multivariate time series and neural network-based load curve models. We then use a nonparametric hypothesis test to compare the daily mean errors of the best forecaster of each kind and, so, verify if considering the air temperature leads to any statistically significant improvement in the forecasting.
Sociedad Argentina de Informática e Investigación Operativa (SADIO)
description Online short-term load forecasts are needed for efficient demand management on power systems. To model the load, univariate and multivariate forecast approaches were developed: while the first consider the load as a linear function of its time series, the other also takes in account the nonlinear effects of weather-related variables (mainly the air temperature). Despite the wide recent literature on multivariate models, some authors state that univariate ones are sufficient for short-term purposes, claiming that including temperature variables unnecessarily elevates the model complexity, putting parsimony and robustness at risk. In this study, we compare the forecasts produced, for real data, by several univariate and multivariate time series and neural network-based load curve models. We then use a nonparametric hypothesis test to compare the daily mean errors of the best forecaster of each kind and, so, verify if considering the air temperature leads to any statistically significant improvement in the forecasting.
publishDate 2015
dc.date.none.fl_str_mv 2015-09
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dc.language.none.fl_str_mv eng
language eng
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dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by/3.0/
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Creative Commons Attribution 3.0 Unported (CC BY 3.0)
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143-151
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