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.

翻译：电力系统运行中面临的多源不确定性无法通过确定性方法完全涵盖。分布预测被用于控制并缓解与这种不确定性相关的风险。近年来深度学习的进展显著提升了点预测的精度，但精确的分布预测仍是一项重大挑战。本文提出了一种能够预测任意分位数的通用分布预测新方法。我们证明该通用方法可无缝应用于两种不同神经网络架构，在短期电力需求预测任务中取得了最先进的分布预测结果。基于欧洲国家35组小时级电力需求时间序列的实证验证了本方法的有效性。相关代码已开源：https://github.com/boreshkinai/any-quantile