Probabilistic load forecasting with Reservoir Computing

TL;DR

This paper investigates the use of reservoir computing for probabilistic load forecasting, emphasizing uncertainty quantification to improve decision-making in power grid management.

Contribution

It explores and compares Bayesian and deterministic uncertainty quantification methods within reservoir computing for the first time.

Findings

Reservoir computing effectively predicts load time series.

Bayesian methods provide more reliable uncertainty estimates.

Deterministic approaches are computationally more efficient.

Abstract

Some applications of deep learning require not only to provide accurate results but also to quantify the amount of confidence in their prediction. The management of an electric power grid is one of these cases: to avoid risky scenarios, decision-makers need both precise and reliable forecasts of, for example, power loads. For this reason, point forecasts are not enough hence it is necessary to adopt methods that provide an uncertainty quantification. This work focuses on reservoir computing as the core time series forecasting method, due to its computational efficiency and effectiveness in predicting time series. While the RC literature mostly focused on point forecasting, this work explores the compatibility of some popular uncertainty quantification methods with the reservoir setting. Both Bayesian and deterministic approaches to uncertainty assessment are evaluated and compared in terms of their prediction accuracy, computational resource efficiency and reliability of the estimated uncertainty, based on a set of carefully chosen performance metrics.