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.

翻译：深度学习的一些应用不仅需要提供准确的结果，还需要量化其预测的置信度。电力系统的管理便是此类场景之一：为避免高风险情况，决策者需要对电力负荷等指标做出既精确又可靠的预测。因此，点预测方法尚不足以满足需求，有必要采用能够提供不确定性量化的方法。本工作以储层计算作为核心时间序列预测方法，因其在时间序列预测中兼具计算效率与有效性。尽管储层计算领域的研究主要聚焦于点预测，本工作探讨了几种主流不确定性量化方法与储层框架的兼容性。基于一组精心选择的性能指标，我们对贝叶斯方法与确定性方法在不确定性评估中的预测精度、计算资源效率以及估计不确定性的可靠性进行了评估与比较。