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

Listed author(s):
  • Pappas, S.Sp.
  • Ekonomou, L.
  • Karamousantas, D.Ch.
  • Chatzarakis, G.E.
  • Katsikas, S.K.
  • Liatsis, P.
Registered author(s):

    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.

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    File URL: http://www.sciencedirect.com/science/article/pii/S0360544208001357
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    Article provided by Elsevier in its journal Energy.

    Volume (Year): 33 (2008)
    Issue (Month): 9 ()
    Pages: 1353-1360

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    Handle: RePEc:eee:energy:v:33:y:2008:i:9:p:1353-1360
    DOI: 10.1016/j.energy.2008.05.008
    Contact details of provider: Web page: http://www.journals.elsevier.com/energy

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    1. Yang, Ming & Yu, Xin, 2004. "China’s rural electricity market—a quantitative analysis," Energy, Elsevier, vol. 29(7), pages 961-977.
    2. Mohamed, Zaid & Bodger, Pat, 2005. "Forecasting electricity consumption in New Zealand using economic and demographic variables," Energy, Elsevier, vol. 30(10), pages 1833-1843.
    3. Hirotugu Akaike, 1969. "Fitting autoregressive models for prediction," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 21(1), pages 243-247, December.
    4. Chen, Changhua & Davis, Richard A. & Brockwell, Peter J., 1996. "Order Determination for Multivariate Autoregressive Processes Using Resampling Methods," Journal of Multivariate Analysis, Elsevier, vol. 57(2), pages 175-190, May.
    5. Weron, R. & Kozłowska, B. & Nowicka-Zagrajek, J., 2001. "Modeling electricity loads in California: a continuous-time approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 299(1), pages 344-350.
    6. Ozturk, Harun Kemal & Ceylan, Halim & Canyurt, Olcay Ersel & Hepbasli, Arif, 2005. "Electricity estimation using genetic algorithm approach: a case study of Turkey," Energy, Elsevier, vol. 30(7), pages 1003-1012.
    7. Joanna Nowicka-Zagrajek & Rafal Weron, 2002. "Modeling electricity loads in California: ARMA models with hyperbolic noise," HSC Research Reports HSC/02/02, Hugo Steinhaus Center, Wroclaw University of Technology.
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