Energy forward price prediction with a hybrid adaptive model

Hang T. Nguyen, Ian T. Nabney

Research output: Chapter in Book/Report/Conference proceedingChapter in a book

3 Citations (Scopus)
11 Downloads (Pure)


This paper presents a forecasting technique for forward electricity/gas prices, one day ahead. This technique combines a Kalman filter (KF) and a generalised autoregressive conditional heteroschedasticity (GARCH) model (often used in financial forecasting). The GARCH model is used to compute next value of a time series. The KF updates parameters of the GARCH model when the new observation is available. This technique is applied to real data from the UK energy markets to evaluate its performance. The results show that the forecasting accuracy is improved significantly by using this hybrid model. The methodology can be also applied to forecasting market clearing prices and electricity/gas loads.
Original languageEnglish
Title of host publicationIEEE Symposium on Computational Intelligence for Financial Engineering, 2009. CIFEr '09
Place of PublicationUnited States
PublisherIEEE Computer Society
Number of pages6
ISBN (Print)9781424427741
Publication statusPublished - 1 Mar 2009

Bibliographical note

Computational Intelligence for Financial Engineering, CIFEr 2009, Nashville (TN)


  • Kalman filters, autoregressive processes, load forecasting, power markets, GARCH model, Kalman filter, UK energy markets, energy forward price prediction, forecasting technique, forward electricity/gas prices, generalised autoregressive conditional heteroschedasticity model, hybrid adaptive model

Fingerprint Dive into the research topics of 'Energy forward price prediction with a hybrid adaptive model'. Together they form a unique fingerprint.

Cite this