Electricity Distributor Load Forecasting with ARMA Models

14 Pages Posted: 4 Aug 2013

See all articles by Mario D. Simões

Mario D. Simões

IAG Business School; IBMEC-RJ

Marcelo Cabus Klotzle

Pontifical Catholic University of Rio de Janeiro (PUC-Rio)

Antonio Carlos Figueiredo Pinto

Independent

Leonardo Gomes

Pontifical Catholic University of Rio de Janeiro (PUC-Rio)

Date Written: January 30, 2013

Abstract

The present work attempts to evaluate the advantages inherent to the use of exogenous variables highly correlated to the electric load, for the forecast of future demand. Here we utilize time series models of the auto-regressive moving average types incorporating seasonal treatment and exogenous variables. The details relevant to good modeling are presented and various different models are adjusted, including different combinations of load against the exogenous regressors IBC-Br and temperature. Considerations regarding non-stationary data such as different data generating processes (DGP) using trend or difference integration alternatives are investigated. In the end we demonstrate that predictions made using ARMA type models do not benefit from further sophistication in the form of exogenous variables due to the error inherent to the method itself, when applied to long term forecasts.

Keywords: electricity, load, forecast, ARMA

JEL Classification: C3, C32, C53, Q47

Suggested Citation

Simões, Mario D. and Klotzle, Marcelo Cabus and Pinto, Antonio Carlos Figueiredo and Gomes, Leonardo, Electricity Distributor Load Forecasting with ARMA Models (January 30, 2013). Available at SSRN: https://ssrn.com/abstract=2305636 or http://dx.doi.org/10.2139/ssrn.2305636

Mario D. Simões (Contact Author)

IAG Business School ( email )

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IBMEC-RJ ( email )

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Marcelo Cabus Klotzle

Pontifical Catholic University of Rio de Janeiro (PUC-Rio) ( email )

Rua Marquas de Sao Vicente, 225
Rio De Janeiro, RJ 22453-900
Brazil

Leonardo Gomes

Pontifical Catholic University of Rio de Janeiro (PUC-Rio) ( email )

Rua Marquas de Sao Vicente, 225
Rio De Janeiro, RJ 22453-900
Brazil

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