Day-ahead electricity price forecasting using wavelet transform combined with ARIMA and GARCH models

This paper proposes a novel price forecasting method based on wavelet transform combined with ARIMA and GARCH models. By wavelet transform, the historical price series is decomposed and reconstructed into one approximation series and some detail series. Then each subseries can be separately predicte...

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Bibliographic Details
Published inApplied energy Vol. 87; no. 11; pp. 3606 - 3610
Main Authors Tan, Zhongfu, Zhang, Jinliang, Wang, Jianhui, Xu, Jun
Format Journal Article
LanguageEnglish
Published Kidlington Elsevier Ltd 01.11.2010
Elsevier
SeriesApplied Energy
Subjects
Applied sciences
Approximation
ARIMA
Composing
Economic data
Electric energy
Electricity
Energy
Energy economics
Exact sciences and technology
Forecasting
GARCH
General, economic and professional studies
Mathematical analysis
Mathematical models
Methodology. Modelling
Price forecasting
Price forecasting Wavelet transform ARIMA GARCH
Time series
Wavelet transform
Wavelet transforms
GARCH
Wavelet transform
Price forecasting
ARIMA
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Summary:This paper proposes a novel price forecasting method based on wavelet transform combined with ARIMA and GARCH models. By wavelet transform, the historical price series is decomposed and reconstructed into one approximation series and some detail series. Then each subseries can be separately predicted by a suitable time series model. The final forecast is obtained by composing the forecasted results of each subseries. This proposed method is examined on Spanish and PJM electricity markets and compared with some other forecasting methods.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:0306-2619
1872-9118
DOI:10.1016/j.apenergy.2010.05.012