Probabilistic forecasting in day-ahead electricity markets: Simulating peak and off-peak prices

Peru Muniain and Florian Ziel

International Journal of Forecasting, 2020, vol. 36, issue 4, 1193-1210

Abstract: In this article 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 ordinary least squares and the elastic net, and the residuals are estimated by maximum likelihood. Our contribution is to include a bivariate jump component in a mean reverting jump diffusion model in the residuals. The models’ forecasts are evaluated with use of four different criteria, including the energy score to measure whether the correlation structure between the time series is properly included. 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 to properly forecast correlated time series.

Keywords: Ensemble forecasting; Energy score; Electricity price forecasting; Dependency structure; Elastic net estimation; Forecasting evaluation; GARCH; Jump diffusion process; Pinball score; Bivariate Bernoulli (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (26)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:36:y:2020:i:4:p:1193-1210

DOI: 10.1016/j.ijforecast.2019.11.006

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