Modeling and Forecasting Electricity Spot Prices: A Functional Data Perspective

Dominik Liebl ()

MPRA Paper from University Library of Munich, Germany

Abstract: Classical time series models have serious difficulties in modeling and forecasting the enormous fluctuations of electricity spot prices. Markov regime switch models belong to the most often used models in the electric- ity literature. These models try to capture the fluctuations of electricity spot prices by using different regimes, each with its own mean and covariance structure. Usually one regime is dedicated to moderate prices and another is dedicated to high prices. However, these models show poor performance and there is no theoretical justification for this kind of classification. The merit or- der model, the most important micro-economic pricing model for electricity spot prices, however, suggests a continuum of mean levels with a functional dependence on electricity demand. We propose a new statistical perspective on modeling and forecasting electricity spot prices that accounts for the merit order model. In a first step, the functional relation between electricity spot prices and electricity demand is modeled by daily price-demand functions. In a second step, we parameter- ize the series of daily price-demand functions using a functional factor model. The power of this new perspective is demonstrated by a forecast study that compares our functional factor model with two established classical time se- ries models as well as two alternative functional data models.

Keywords: Functional factor model; functional data analysis; time series analysis; fundamental market model; merit order curve; European Energy Exchange; EEX (search for similar items in EconPapers)
JEL-codes: C1 C14 C5 (search for similar items in EconPapers)
Date: 2013-09
New Economics Papers: this item is included in nep-ecm, nep-ene and nep-for
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (48)

Published in The Annals of Applied Statistics 3.7(2013): pp. 1562-1592

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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:50881

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