Multivariate Scenario Generation of Day-Ahead Electricity Prices using Normalizing Flows
This addresses the need for accurate uncertainty-aware price predictions for electricity market traders, representing an incremental improvement with domain-specific adaptations.
The paper tackles probabilistic forecasting of day-ahead electricity prices, which is challenging due to non-stationarity from market changes like the 2021 energy crisis, by using normalizing flows with extended features and periodic retraining to generate high-quality price scenarios that reproduce the true distribution and yield accurate forecasts.
Trading on the day-ahead electricity markets requires accurate information about the realization of electricity prices and the uncertainty attached to the predictions. Deriving accurate forecasting models presents a difficult task due to the day-ahead price's non-stationarity resulting from changing market conditions, e.g., due to changes resulting from the energy crisis in 2021. We present a probabilistic forecasting approach for day-ahead electricity prices using the fully data-driven deep generative model called normalizing flow. Our modeling approach generates full-day scenarios of day-ahead electricity prices based on conditional features such as residual load forecasts. Furthermore, we propose extended feature sets of prior realizations and a periodic retraining scheme that allows the normalizing flow to adapt to the changing conditions of modern electricity markets. Our results highlight that the normalizing flow generates high-quality scenarios that reproduce the true price distribution and yield accurate forecasts. Additionally, our analysis highlights how our improvements towards adaptations in changing regimes allow the normalizing flow to adapt to changing market conditions and enable continued sampling of high-quality day-ahead price scenarios.