Energy forward price prediction with a hybrid adaptive model

Hang T. Nguyen; Ian T. Nabney

doi:10.1109/CIFER.2009.4937504

Energy forward price prediction with a hybrid adaptive model

Hang T. Nguyen, Ian T. Nabney

Research output: Chapter in Book/Report/Conference proceeding › Chapter in a book

3 Citations (Scopus)

11 Downloads (Pure)

Abstract

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 language	English
Title of host publication	IEEE Symposium on Computational Intelligence for Financial Engineering, 2009. CIFEr '09
Place of Publication	United States
Publisher	IEEE Computer Society
Pages	66-71
Number of pages	6
ISBN (Print)	9781424427741
DOIs	https://doi.org/10.1109/CIFER.2009.4937504
Publication status	Published - 1 Mar 2009

Bibliographical note

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

Keywords

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

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title = "Energy forward price prediction with a hybrid adaptive model",

abstract = "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.",

keywords = "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",

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AU - Nabney, Ian T.

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Y1 - 2009/3/1

N2 - 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.

AB - 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.

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DO - 10.1109/CIFER.2009.4937504

M3 - Chapter in a book

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BT - IEEE Symposium on Computational Intelligence for Financial Engineering, 2009. CIFEr '09

PB - IEEE Computer Society

CY - United States

ER -

Energy forward price prediction with a hybrid adaptive model

Abstract

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Keywords

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