Combining the wavelet transform and forecasting models to predict gas forward prices

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Abstract

This paper presents a forecasting technique for forward energy prices, one day ahead. This technique combines a wavelet transform and forecasting models such as multi- layer perceptron, linear regression or GARCH. These techniques are applied to real data from the UK gas markets to evaluate their performance. The results show that the forecasting accuracy is improved significantly by using the wavelet transform. The methodology can be also applied to forecasting market clearing prices and electricity/gas loads.

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  • ICMLA08final.pdf

    Rights statement: Seventh International Conference on Machine Learning and Applications, San Diego (US)

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Details

Publication date11 Dec 2008
Publication titleSeventh International Conference on Machine Learning and Applications, 2008. ICMLA '08
PublisherIEEE
Pages311-317
Number of pages7
ISBN (Print)9780769534954
Original languageEnglish

Bibliographic note

Seventh International Conference on Machine Learning and Applications, San Diego (US)

    Keywords

  • forecasting theory, natural gas technology, power markets, power system economics, pricing, wavelet transforms, UK gas market, electricity load, forecasting model, forward energy price, gas forward price prediction, gas load, market clearing price forecasting, wavelet transform, GARCH, linear regression, multi-layer perceptron

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