LAPSE:2019.0271
Published Article
LAPSE:2019.0271
Parametric Density Recalibration of a Fundamental Market Model to Forecast Electricity Prices
Antonio Bello, Derek Bunn, Javier Reneses, Antonio Muñoz
February 5, 2019
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.
Keywords
densities, electricity, forecasting, fundamentals, hybrid, prices
Suggested Citation
Bello A, Bunn D, Reneses J, Muñoz A. Parametric Density Recalibration of a Fundamental Market Model to Forecast Electricity Prices. (2019). LAPSE:2019.0271
Author Affiliations
Bello A: Institute for Research in Technology, Technical School of Engineering (ICAI), Universidad Pontificia Comillas, 28015 Madrid, Spain
Bunn D: London Business School, London NW1 4SA, UK [ORCID]
Reneses J: Institute for Research in Technology, Technical School of Engineering (ICAI), Universidad Pontificia Comillas, 28015 Madrid, Spain
Muñoz A: Institute for Research in Technology, Technical School of Engineering (ICAI), Universidad Pontificia Comillas, 28015 Madrid, Spain
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Journal Name
Energies
Volume
9
Issue
11
Article Number
E959
Year
2016
Publication Date
2016-11-17
Published Version
ISSN
1996-1073
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PII: en9110959, Publication Type: Journal Article
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LAPSE:2019.0271
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doi:10.3390/en9110959
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Feb 5, 2019
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[v1] (Original Submission)
Feb 5, 2019
 
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Calvin Tsay
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