Probabilistic solar irradiance forecasting has received widespread attention in recent years, as it provides more uncertainty information for the future photovoltaic generation. In this study, a hybrid probabilistic solar irradiance prediction method is proposed, which combines a deep recurrent neural network and residual modeling. Specifically, the long short‐term memory‐based point prediction using historical records and related features is applied to obtain deterministic forecasts. Next, these deterministic forecasts are employed as inputs to estimate the residual distributions. Furthermore, maximum likelihood estimation is utilized to compute the parameters of the residual distribution. Finally, the point prediction and residual distribution jointly generate the final probabilistic forecasting results. Compared with other deterministic and probabilistic forecasting models, the proposed method yields promising results on a publicly available dataset. © 2020 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

中文翻译：

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通过基于深度学习的混合方法进行概率太阳辐照度预测

概率太阳辐照度预测近年来已受到广泛关注，因为它为未来的光伏发电提供了更多不确定性信息。在这项研究中，提出了一种混合概率太阳辐照度预测方法，该方法结合了深度递归神经网络和残差建模。具体来说，使用历史记录和相关功能的基于短期记忆的长期点预测可用于获得确定性预测。接下来，将这些确定性预测用作估计残差分布的输入。此外，利用最大似然估计来计算残差分布的参数。最后，点预测和残差分布共同生成最终的概率预测结果。与其他确定性和概率性预测模型相比，该方法在可公开获得的数据集上产生了可喜的结果。©2020日本电气工程师学会。由Wiley Periodicals LLC发布。