  Power systems operate under uncertainty originating from multiple factors
that are impossible to account for deterministically. Distributional
forecasting is used to control and mitigate risks associated with this
uncertainty. Recent progress in deep learning has helped to significantly
improve the accuracy of point forecasts, while accurate distributional
forecasting still presents a significant challenge. In this paper, we propose a
novel general approach for distributional forecasting capable of predicting
arbitrary quantiles. We show that our general approach can be seamlessly
applied to two distinct neural architectures leading to the state-of-the-art
distributional forecasting results in the context of short-term electricity
demand forecasting task. We empirically validate our method on 35 hourly
electricity demand time-series for European countries. Our code is available
here: https://github.com/boreshkinai/any-quantile.
