Any-Quantile Probabilistic Forecasting of Short-Term Electricity Demand

TL;DR

This paper introduces a novel neural network-based approach for distributional forecasting that predicts arbitrary quantiles, significantly improving short-term electricity demand predictions across European countries.

Contribution

The paper presents a new general method for distributional forecasting that can be applied to different neural architectures, achieving state-of-the-art results.

Findings

Achieved state-of-the-art distributional forecasting accuracy.

Validated on 35 European electricity demand time-series.

Demonstrated flexibility across neural network architectures.

Abstract

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.