论文标题
时空预测的深度学习 - 适用于太阳能
Deep learning for spatio-temporal forecasting -- application to solar energy
论文作者
论文摘要
本文通过深度学习解决了时空预测的主题。在法国电力公司（EDF）中的激励应用是用鱼眼图像预测的短期太阳能预测。我们探索了两个主要的研究方向，可以通过注入外部物理知识来改善深度预测方法。第一个方向涉及训练损失功能的作用。我们表明，可以利用可区分的形状和时间标准来改善现有模型的性能。我们通过提出的扩张损失函数和条纹模型的概率上下文解决了确定性上下文。我们的第二个方向是使用深度数据驱动网络来增强不完整的物理模型，以进行准确的预测。为了进行视频预测，我们介绍了植物模型，该模型将物理动态与预测所需的残留信息（例如纹理或细节）相关。我们进一步提出了一个学习框架（Aphynity），以确保在轻度假设下物理和数据驱动组件之间有原则的独特的线性分解，从而导致更好的预测性能和参数识别。
This thesis tackles the subject of spatio-temporal forecasting with deep learning. The motivating application at Electricity de France (EDF) is short-term solar energy forecasting with fisheye images. We explore two main research directions for improving deep forecasting methods by injecting external physical knowledge. The first direction concerns the role of the training loss function. We show that differentiable shape and temporal criteria can be leveraged to improve the performances of existing models. We address both the deterministic context with the proposed DILATE loss function and the probabilistic context with the STRIPE model. Our second direction is to augment incomplete physical models with deep data-driven networks for accurate forecasting. For video prediction, we introduce the PhyDNet model that disentangles physical dynamics from residual information necessary for prediction, such as texture or details. We further propose a learning framework (APHYNITY) that ensures a principled and unique linear decomposition between physical and data-driven components under mild assumptions, leading to better forecasting performances and parameter identification.
