ECLIPSE：设想云引起的太阳能扰动
ECLIPSE : Envisioning CLoud Induced Perturbations in Solar Energy
摘要：
将太阳能有效地纳入电力组合取决于对其不可逆性的可靠预测。一个有前途的方法来预测的时间变化的太阳辐照度所造成的云量动态的基础上的分析序列的地面拍摄的天空图像或卫星观测。尽管结果令人鼓舞，但现有深度学习方法的一个经常性限制在于对过去的观察做出反应而不是积极预测未来事件的普遍趋势。这导致了频繁的时间滞后和有限的能力来预测突发事件。为了应对这一挑战，我们引入了ECLIPSE，这是一种时空神经网络架构，可以从天空图像中对云的运动进行建模，不仅可以预测未来的辐照度水平和相关的不确定性，还可以分割图像，从而提供更丰富的局部辐照度图信息。我们表明，ECLIPSE预期的关键事件，并减少时间延迟，同时产生视觉上逼真的未来。模型的特点和属性进行了研究，烧蚀研究和比较研究的好处和不同的方式来集成辅助数据到建模。模型预测还通过分析在网络训练期间学习的主要时空分量来解释。
英文摘要：
Efficient integration of solar energy into the electricity mix depends on a reliable anticipation of its intermittency. A promising approach to forecast the temporal variability of solar irradiance resulting from the cloud cover dynamics is based on the analysis of sequences of ground-taken sky images or satellite observations. Despite encouraging results, a recurrent limitation of existing deep learning approaches lies in the ubiquitous tendency of reacting to past observations rather than actively anticipating future events. This leads to a frequent temporal lag and limited ability to predict sudden events. To address this challenge, we introduce ECLIPSE, a spatio-temporal neural network architecture that models cloud motion from sky images to not only predict future irradiance levels and associated uncertainties, but also segmented images, which provide richer information on the local irradiance map. We show that ECLIPSE anticipates critical events and reduces temporal delay while generating visually realistic futures. The model characteristics and properties are investigated with an ablation study and a comparative study on the benefits and different ways to integrate auxiliary data into the modelling. The model predictions are also interpreted through an analysis of the principal spatio-temporal components learned during network training.
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