FORECASTING RESIDENTIAL ELECTRICITY CONSUMPTION IN BRAZIL: APPLICATION OF THE ARX MODEL
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Data de Publicação: | 2010 |
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Tipo de documento: | Artigo |
Idioma: | eng por |
Título da fonte: | Future Studies Research Journal: Trends and Strategies |
Texto Completo: | https://www.revistafuture.org/FSRJ/article/view/50 |
Resumo: | This work aims to propose the application of the ARX model to forecast residential electricity consumption in Brazil. Such estimates are critical for decision making in the energy sector, from a technical, economic and environmentally sustainable standpoint. The demand for electricity follows a multiplicative model based on economic theory and involves four explanatory variables: the cost of residential electricity, the actual average income, the inflation of domestic utilities and the electricity consumption. The coefficients of the electricity consumption equation were determined using the ARX model, which considers the influence of exogenous variables to estimate the dependent variable and employs an autoregression process for residual modeling to improve the explanatory power. The resulting model has a determination coefficient of 95.4 percent and all estimated coefficients were significant at the 0.10 descriptive level. Residential electricity consumption estimates were also determined for January and February 2010 within the 95 percent confidence interval, which included the actual consumption figures observed. The proposed model has been shown to be useful for estimating residential electricity consumption in Brazil. Key-words: Time series. Electricity consumption. ARX modeling. |
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Future Studies Research Journal: Trends and Strategies |
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FORECASTING RESIDENTIAL ELECTRICITY CONSUMPTION IN BRAZIL: APPLICATION OF THE ARX MODELPREVISÃO DO CONSUMO RESIDENCIAL DE ENERGIA ELÉTRICA NO BRASIL: APLICAÇÃO DO MODELO ARX DOI:10.7444/fsrj.v2i2.50This work aims to propose the application of the ARX model to forecast residential electricity consumption in Brazil. Such estimates are critical for decision making in the energy sector, from a technical, economic and environmentally sustainable standpoint. The demand for electricity follows a multiplicative model based on economic theory and involves four explanatory variables: the cost of residential electricity, the actual average income, the inflation of domestic utilities and the electricity consumption. The coefficients of the electricity consumption equation were determined using the ARX model, which considers the influence of exogenous variables to estimate the dependent variable and employs an autoregression process for residual modeling to improve the explanatory power. The resulting model has a determination coefficient of 95.4 percent and all estimated coefficients were significant at the 0.10 descriptive level. Residential electricity consumption estimates were also determined for January and February 2010 within the 95 percent confidence interval, which included the actual consumption figures observed. The proposed model has been shown to be useful for estimating residential electricity consumption in Brazil. Key-words: Time series. Electricity consumption. ARX modeling. O objetivo deste trabalho é propor a aplicação do modelo ARX para fazer previsões do consumo residencial de energia elétrica no Brasil, fundamentais para suportar decisões do setor de energia em bases técnica, econômica e ambientalmente sustentáveis. A função da demanda de energia elétrica foi modelada segundo um modelo multiplicativo com base na teoria econômica e incluiu quatro variáveis explicativas: tarifa residencial de energia elétrica, rendimento médio real das pessoas ocupadas, índice de inflação de utilidades domésticas e consumo de energia. Para estimar os coeficientes da equação do consumo de energia elétrica, foi utilizado o modelo ARX, o qual considera na projeção da variável dependente a influência de variáveis exógenas e modela os resíduos por meio de um processo auto-regressivo a fim de aumentar seu poder explicativo. Resultados indicaram um coeficiente de determinação de 95,4% e todos os coeficientes estimados foram estatisticamente significantes a um nível descritivo de 0,10. Foram realizadas projeções do consumo residencial de energia elétrica para os meses de janeiro e fevereiro de 2010 no intervalo de confiança a 95% o qual incluiu os valores verdadeiros de consumo observados. O modelo proposto mostrou ótima performance na previsão do consumo de energia residencial do país. Palavras-chave: Séries temporais. Energia elétrica. Modelo ARXFuture Studies Research Journal: Trends and Strategies2010-12-07info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionPeer-reviewed Articleapplication/pdfapplication/pdfhttps://www.revistafuture.org/FSRJ/article/view/5010.24023/FutureJournal/2175-5825/2010.v2i2.50Future Studies Research Journal: Trends and Strategies; Vol. 2 No. 2 (2010): Future Studies Research Journal; 3-16Future Studies Research Journal: Trends and Strategies [FSRJ]; v. 2 n. 2 (2010): Future Studies Research Journal; 3-162175-5825reponame:Future Studies Research Journal: Trends and Strategiesinstname:Fundação Instituto de Administração (FIA)instacron:FIAengporhttps://www.revistafuture.org/FSRJ/article/view/50/101https://www.revistafuture.org/FSRJ/article/view/50/90Copyright (c) 2011 Future Studies Research Journal: Trends and Strategiesinfo:eu-repo/semantics/openAccessde Castro, Joao BoscoMontini, Alessandra de Ávila2018-07-19T15:46:01Zoai:ojs.future.emnuvens.com.br:article/50Revistahttps://www.revistafuture.org/FSRJ/oai2175-58252175-5825opendoar:2018-07-19T15:46:01Future Studies Research Journal: Trends and Strategies - Fundação Instituto de Administração (FIA)false |
dc.title.none.fl_str_mv |
FORECASTING RESIDENTIAL ELECTRICITY CONSUMPTION IN BRAZIL: APPLICATION OF THE ARX MODEL PREVISÃO DO CONSUMO RESIDENCIAL DE ENERGIA ELÉTRICA NO BRASIL: APLICAÇÃO DO MODELO ARX DOI:10.7444/fsrj.v2i2.50 |
title |
FORECASTING RESIDENTIAL ELECTRICITY CONSUMPTION IN BRAZIL: APPLICATION OF THE ARX MODEL |
spellingShingle |
FORECASTING RESIDENTIAL ELECTRICITY CONSUMPTION IN BRAZIL: APPLICATION OF THE ARX MODEL de Castro, Joao Bosco |
title_short |
FORECASTING RESIDENTIAL ELECTRICITY CONSUMPTION IN BRAZIL: APPLICATION OF THE ARX MODEL |
title_full |
FORECASTING RESIDENTIAL ELECTRICITY CONSUMPTION IN BRAZIL: APPLICATION OF THE ARX MODEL |
title_fullStr |
FORECASTING RESIDENTIAL ELECTRICITY CONSUMPTION IN BRAZIL: APPLICATION OF THE ARX MODEL |
title_full_unstemmed |
FORECASTING RESIDENTIAL ELECTRICITY CONSUMPTION IN BRAZIL: APPLICATION OF THE ARX MODEL |
title_sort |
FORECASTING RESIDENTIAL ELECTRICITY CONSUMPTION IN BRAZIL: APPLICATION OF THE ARX MODEL |
author |
de Castro, Joao Bosco |
author_facet |
de Castro, Joao Bosco Montini, Alessandra de Ávila |
author_role |
author |
author2 |
Montini, Alessandra de Ávila |
author2_role |
author |
dc.contributor.author.fl_str_mv |
de Castro, Joao Bosco Montini, Alessandra de Ávila |
description |
This work aims to propose the application of the ARX model to forecast residential electricity consumption in Brazil. Such estimates are critical for decision making in the energy sector, from a technical, economic and environmentally sustainable standpoint. The demand for electricity follows a multiplicative model based on economic theory and involves four explanatory variables: the cost of residential electricity, the actual average income, the inflation of domestic utilities and the electricity consumption. The coefficients of the electricity consumption equation were determined using the ARX model, which considers the influence of exogenous variables to estimate the dependent variable and employs an autoregression process for residual modeling to improve the explanatory power. The resulting model has a determination coefficient of 95.4 percent and all estimated coefficients were significant at the 0.10 descriptive level. Residential electricity consumption estimates were also determined for January and February 2010 within the 95 percent confidence interval, which included the actual consumption figures observed. The proposed model has been shown to be useful for estimating residential electricity consumption in Brazil. Key-words: Time series. Electricity consumption. ARX modeling. |
publishDate |
2010 |
dc.date.none.fl_str_mv |
2010-12-07 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://www.revistafuture.org/FSRJ/article/view/50 10.24023/FutureJournal/2175-5825/2010.v2i2.50 |
url |
https://www.revistafuture.org/FSRJ/article/view/50 |
identifier_str_mv |
10.24023/FutureJournal/2175-5825/2010.v2i2.50 |
dc.language.iso.fl_str_mv |
eng por |
language |
eng por |
dc.relation.none.fl_str_mv |
https://www.revistafuture.org/FSRJ/article/view/50/101 https://www.revistafuture.org/FSRJ/article/view/50/90 |
dc.rights.driver.fl_str_mv |
Copyright (c) 2011 Future Studies Research Journal: Trends and Strategies info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2011 Future Studies Research Journal: Trends and Strategies |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf application/pdf |
dc.publisher.none.fl_str_mv |
Future Studies Research Journal: Trends and Strategies |
publisher.none.fl_str_mv |
Future Studies Research Journal: Trends and Strategies |
dc.source.none.fl_str_mv |
Future Studies Research Journal: Trends and Strategies; Vol. 2 No. 2 (2010): Future Studies Research Journal; 3-16 Future Studies Research Journal: Trends and Strategies [FSRJ]; v. 2 n. 2 (2010): Future Studies Research Journal; 3-16 2175-5825 reponame:Future Studies Research Journal: Trends and Strategies instname:Fundação Instituto de Administração (FIA) instacron:FIA |
instname_str |
Fundação Instituto de Administração (FIA) |
instacron_str |
FIA |
institution |
FIA |
reponame_str |
Future Studies Research Journal: Trends and Strategies |
collection |
Future Studies Research Journal: Trends and Strategies |
repository.name.fl_str_mv |
Future Studies Research Journal: Trends and Strategies - Fundação Instituto de Administração (FIA) |
repository.mail.fl_str_mv |
|
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