Abstract
Stochastic models of electricity spot prices depend on price spikes and long-term seasonality. Therefore it is crucial to determine suitable methods for the identification of price spikes and the modeling of long-term seasonal components (LTSC). Following recent studies (Janczura and Weron, 2010; Janczura et al., 2013), we compare the proportion of observations identified as outliers for five different outlier detection methods and three approaches to long-term seasonality modeling. After removing the effects of outliers, we compare the out-of-sample forecasting performance for three categories of long-term seasonality models: dummies, Fourier series, and wavelet-based methods. We consider various combinations of each approach and perform a comprehensive backtesting comparison at different forecasting horizons for the recently liberalized Turkish electricity market.
Original language | English |
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Pages (from-to) | 1-18 |
Number of pages | 18 |
Journal | Bogazici Journal |
Volume | 28 |
Issue number | 2 |
DOIs | |
Publication status | Published - 2014 |
Externally published | Yes |
Keywords
- Electricity price spikes
- Long-term seasonality modeling
- Turkish electricity prices
- Wavelets