English(EN) Two-stage residual learning for calibrated uncertainty in solar irradiance forecasts

新的两阶段残差学习方法提高了太阳辐照度预测的不确定性

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-06 05:00

研究人员开发了一种新颖的两阶段残差学习方法，以提高太阳辐照度预测的准确性，特别是在估计不确定性方面。该方法使用第一个长短期记忆（LSTM）网络来预测预期的辐照度，第二个网络来模拟预测误差。通过单独模拟误差，该方法更准确地捕捉了总预测不确定性，这对于可再生能源发电的决策至关重要。 AI

影响通过改进机器学习模型中的不确定性估计，增强了可再生能源预测的可靠性。

排序理由该集群包含一篇详细介绍新机器学习方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · RR Flanagan, E Kasha, PW Talbot, AS Epiny, MR Deinert · 2026-11-06 05:00

用于太阳辐照度预测中校准不确定性的两阶段残差学习

arXiv:2611.07444v1 Announce Type: new Abstract: Accurate forecasting of renewable generation requires not only point predictions but also reliable estimates of the uncertainty attached to them, particularly at extended horizons. Machine learning models are increasingly used to pr…