Probabilistic forecasting in day-ahead electricity markets: Simulating peak and off-peak prices
By P. Muniain, Florian Ziel. International Journal of Forecasting, 2018
47 citations recorded by the source corpus.
In this paper we include dependency structures for electricity price forecasting and forecasting evaluation. We work with off-peak and peak time series from the German-Austrian day-ahead price, hence we analyze bivariate data. We first estimate the mean of the two time series, and then in a second step we estimate the residuals. The mean equation is estimated by OLS and elastic net and the residuals are estimated by maximum likelihood. Our contribution is to include a bivariate jump component on a mean reverting jump diffusion model in the residuals. The models' forecasts are evaluated using four different criteria, including the energy score to measure whether the correlation structure between the time series is properly included or not. In the results it is observed that the models with bivariate jumps provide better results with the energy score, which means that it is important to consider this structure in order to properly forecast correlated time series.
Subfields: Economics and Econometrics, Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/20e5d053b2d694ebb351ae9ca565952f65e94796
How researchers see this paper
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In Economics and Econometrics
- Grzegorz Marcjasz
- B. Schutter
- Jesus Lago
- Rafal Weron
- Florian Ziel
- Ladislav Kristoufek
- Jakub Nowotarski
- R. Steinert
In Electrical and Electronic Engineering
- Grzegorz Marcjasz
- B. Schutter
- Jesus Lago
- Rafal Weron
- Florian Ziel
- Jakub Nowotarski
- Bartosz Uniejewski
- Stefan Trück
Citations in the corpus
- Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark (2020, methodology): “…regressors is large, using the least absolute shrinkage and selection operator (LASSO) [56] or its generalization the elastic net [66] as implicit feature selection methods have been shown to improve the forecasting results [55,57,64,67–69], also in intraday [70,71] and probabilistic [72,73] EPF.”