Electricity demand loads modeling using AutoRegressive Moving Average (ARMA) models

This study addresses the problem of modeling the electricity demand loads in Greece. The provided actual load data is deseasonilized and an AutoRegressive Moving Average (ARMA) model is fitted on the data off-line, using the Akaike Corrected Information Criterion (AICC). The developed model fits the...

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Bibliographic Details
Published in:Energy (Oxford) 2008-09, Vol.33 (9), p.1353-1360
Main Authors: Pappas, S.Sp, Ekonomou, L., Karamousantas, D.Ch, Chatzarakis, G.E., Katsikas, S.K., Liatsis, P.
Format: Article
Language:English
Subjects:
Adaptive multi-model filtering
Applied sciences
ARMA
Economic data
Electric energy
Electricity demand load
Energy
Energy economics
Exact sciences and technology
General, economic and professional studies
Kalman filter
Methodology. Modelling
Order selection
Parameter estimation
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Summary:This study addresses the problem of modeling the electricity demand loads in Greece. The provided actual load data is deseasonilized and an AutoRegressive Moving Average (ARMA) model is fitted on the data off-line, using the Akaike Corrected Information Criterion (AICC). The developed model fits the data in a successful manner. Difficulties occur when the provided data includes noise or errors and also when an on-line/adaptive modeling is required. In both cases and under the assumption that the provided data can be represented by an ARMA model, simultaneous order and parameter estimation of ARMA models under the presence of noise are performed. The produced results indicate that the proposed method, which is based on the multi-model partitioning theory, tackles successfully the studied problem. For validation purposes the produced results are compared with three other established order selection criteria, namely AICC, Akaike's Information Criterion (AIC) and Schwarz's Bayesian Information Criterion (BIC). The developed model could be useful in the studies that concern electricity consumption and electricity prices forecasts.
ISSN:0360-5442
DOI:10.1016/j.energy.2008.05.008