Modeling and forecasting electricity spot prices: A functional data perspective

By Dominik Liebl. 2013

129 citations recorded by the source corpus.

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.

Subfields: Economics and Econometrics, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/676bc2d5c8f1606f63cb173e15e3460c5c4f0add

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Citations in the corpus

Forecasting Functional Time Series with a New Hilbertian ARMAX Model: Application to Electricity Price Forecasting (2018, background): “Some examples of price forecasting with functional methods can be found in [12] and [13].”
On the optimal reconstruction of partially observed functional data (2017, methodology): “682 MSEratio = MSE/min(MSE) ity spot prices (Liebl, 2013; Weron, 2014) and for testing price differences (Liebl, 2019).”
Efficient modeling and forecasting of electricity spot prices (2014, unknown): “Moreover, we remark that the usage of the basic model of Liebl (2013) as a benchmark to our model is limited, as his approach was tailor-made for the exclusion of special days like weekends and heavy outliers.”
Electricity price forecasting: A review of the state-of-the-art with a look into the future (2014, methodology): “…have been suggested in the literature, ranging from mean-reverting diffusions (Karakatsani & Bunn, 2008), to Gaussian (Huisman & de Jong, 2003; Liebl, 2013), lognormal (Weron, Bierbrauer et al., 2004), exponential (Bierbrauer, Menn, Rachev, & Trück, 2007), heavy tailed (Bierbrauer, Trück, &…”
Functional linear regression with functional response (2017, methodology): “We adopt Tikhonov regularization (see Kress, 1999 and Carrasco, Florens, and Renault, 2007).”