Bayesian Hierarchical Probabilistic Forecasting of Intraday Electricity Prices
This work addresses the problem of handling large uncertainties in electricity price forecasting for continuous intraday markets, which is incremental as it applies Bayesian methods to a specific domain with novel performance gains.
The study tackled forecasting intraday electricity prices in the German market by developing a Bayesian hierarchical probabilistic method that incorporates parameter uncertainty and covariables, resulting in significant improvements such as a 5.9% reduction in absolute errors and a 1.7% increase in accuracy compared to benchmarks.
We address the need for forecasting methodologies that handle large uncertainties in electricity prices for continuous intraday markets by incorporating parameter uncertainty and using a broad set of covariables. This study presents the first Bayesian forecasting of electricity prices traded on the German intraday market. Endogenous and exogenous covariables are handled via Orthogonal Matching Pursuit (OMP) and regularising priors. The target variable is the IDFull price index, with forecasts given as posterior predictive distributions. Validation uses the highly volatile 2022 electricity prices, which have seldom been studied. As a benchmark, we use all intraday transactions at the time of forecast to compute a live IDFull value. According to market efficiency, it should not be possible to improve on this last-price benchmark. However, we observe significant improvements in point measures and probability scores, including an average reduction of $5.9\,\%$ in absolute errors and an average increase of $1.7\,\%$ in accuracy when forecasting whether the IDFull exceeds the day-ahead price. Finally, we challenge the use of LASSO in electricity price forecasting, showing that OMP results in superior performance, specifically an average reduction of $22.7\,\%$ in absolute error and $20.2\,\%$ in the continuous ranked probability score.