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
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/59432
Ver los metadatos del registro completo
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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 |
dc.type.none.fl_str_mv |
info:eu-repo/semantics/conferenceObject info:eu-repo/semantics/publishedVersion Objeto de conferencia http://purl.org/coar/resource_type/c_5794 info:ar-repo/semantics/documentoDeConferencia |
format |
conferenceObject |
status_str |
publishedVersion |
dc.identifier.none.fl_str_mv |
http://sedici.unlp.edu.ar/handle/10915/59432 |
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http://sedici.unlp.edu.ar/handle/10915/59432 |
dc.language.none.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
info:eu-repo/semantics/altIdentifier/url/http://44jaiio.sadio.org.ar/sites/default/files/sio143-151.pdf info:eu-repo/semantics/altIdentifier/issn/2451-7550 |
dc.rights.none.fl_str_mv |
info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/3.0/ Creative Commons Attribution 3.0 Unported (CC BY 3.0) |
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openAccess |
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http://creativecommons.org/licenses/by/3.0/ Creative Commons Attribution 3.0 Unported (CC BY 3.0) |
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application/pdf 143-151 |
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