Combining day-ahead forecasts for British electricity prices

This paper considers how well the approach of combining forecasts extends to the context of electricity prices. With the increasing popularity of regime switching and time-varying parameter models for predicting power prices, the multi model and evolutionary considerations that usually support the c...

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Bibliographic Details
Published in:Energy economics 2013-01, Vol.35, p.88-103
Main Authors: Bordignon, Silvano, Bunn, Derek W., Lisi, Francesco, Nan, Fany
Format: Article
Language:English
Subjects:
ARMAX
British
Economic models
Economics
Electric power
Electric power generation
Electric rates
Electricity
Electricity price forecasting
Energy economics
Energy policy
Excursions
Forecasting
Forecasting techniques
Forecasts combination
Individuals
Markov regime switching
Mathematical models
Prediction accuracy
Prices
Pricing
Risk management
Simulation
Studies
Switching
Time series
Time-varying parameter regression
Uncertainty
Volatility
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Summary:This paper considers how well the approach of combining forecasts extends to the context of electricity prices. With the increasing popularity of regime switching and time-varying parameter models for predicting power prices, the multi model and evolutionary considerations that usually support the combining of simpler time series methods may be less applicable when the individual models incorporate these features. We address this question with a backtesting analysis on British day-ahead prices. Furthermore, given the volatility of power prices and concerns about accurate forecasting under extreme price excursions, we evaluate the results using various error metrics including expected shortfall. The comparisons are furthermore carefully simulated to consider model selection uncertainty in order to realistically test the value of combining as an ex ante policy. Overall, our results support combining for both accurate operational planning and risk management. ► We study combined vs individual forecasts for British electricity prices prediction. ► Prediction is evaluated by means of several indicators and statistical tests. ► Combined forecasts are more accurate or at least equivalent than individual ones. ► Ex ante, combining is less risky than choosing individual models. ► Equally weighting is a competitive combining method.
ISSN:0140-9883
1873-6181
DOI:10.1016/j.eneco.2011.12.001