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Energies 2016, 9(11), 959;

Parametric Density Recalibration of a Fundamental Market Model to Forecast Electricity Prices

Institute for Research in Technology, Technical School of Engineering (ICAI), Universidad Pontificia Comillas, 28015 Madrid, Spain
London Business School, London NW1 4SA, UK
Author to whom correspondence should be addressed.
Academic Editors: Javier Contreras and John Ringwood
Received: 18 August 2016 / Revised: 3 October 2016 / Accepted: 11 November 2016 / Published: 17 November 2016
(This article belongs to the Special Issue Forecasting Models of Electricity Prices)
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This paper proposes a new approach to hybrid forecasting methodology, characterized as the statistical recalibration of forecasts from fundamental market price formation models. Such hybrid methods based upon fundamentals are particularly appropriate to medium term forecasting and in this paper the application is to month-ahead, hourly prediction of electricity wholesale prices in Spain. The recalibration methodology is innovative in seeking to perform the recalibration into parametrically defined density functions. The density estimation method selects from a wide diversity of general four-parameter distributions to fit hourly spot prices, in which the first four moments are dynamically estimated as latent functions of the outputs from the fundamental model and several other plausible exogenous drivers. The proposed approach demonstrated its effectiveness against benchmark methods across the full range of percentiles of the price distribution and performed particularly well in the tails. View Full-Text
Keywords: electricity; prices; forecasting; fundamentals; hybrid; densities electricity; prices; forecasting; fundamentals; hybrid; densities

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Bello, A.; Bunn, D.; Reneses, J.; Muñoz, A. Parametric Density Recalibration of a Fundamental Market Model to Forecast Electricity Prices. Energies 2016, 9, 959.

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