Probabilistic Forecasts of Load, Solar and Wind for Electricity Price Forecasting

Abstract: Electricity price forecasting is a critical tool for the efficient operation of power systems and for supporting informed decision-making by market participants. This paper explores a novel methodology aimed at improving the accuracy of electricity price forecasts by incorporating probabilistic inputs of fundamental variables. Traditional approaches often rely on point forecasts of exogenous variables such as load, solar, and wind generation. Our method proposes the integration of quantile forecasts of these fundamental variables, providing a new set of exogenous variables that account for a more comprehensive representation of uncertainty. We conducted empirical tests on the German electricity market using recent data to evaluate the effectiveness of this approach. The findings indicate that incorporating probabilistic forecasts of load and renewable energy source generation significantly improves the accuracy of point forecasts of electricity prices. Furthermore, the results clearly show that the highest improvement in forecast accuracy can be achieved with full probabilistic forecast information. This highlights the importance of probabilistic forecasting in research and practice, particularly that the current state-of-the-art in reporting load, wind and solar forecast is insufficient.