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.
| Original language | English |
|---|---|
| Title of host publication | Seventh International Conference on Machine Learning and Applications, 2008. ICMLA '08 |
| Publisher | IEEE |
| Pages | 311-317 |
| Number of pages | 7 |
| ISBN (Print) | 9780769534954 |
| DOIs | |
| Publication status | Published - 11 Dec 2008 |
Bibliographical 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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Cite this
Nguyen, H. T., & Nabney, I. T. (2008). Combining the wavelet transform and forecasting models to predict gas forward prices. In Seventh International Conference on Machine Learning and Applications, 2008. ICMLA '08 (pp. 311-317). IEEE. https://doi.org/10.1109/ICMLA.2008.37