Irradiance Forecasting through Analysis of Sky Images

未来几十年减少全球排放的迫切需要迫使各国努力实现更可持续的发展模式。除了引入必要的需求侧缓解方案外，国际专家组还建议各国政府进行深度能源转型，从而实现供应侧的逐步脱碳。因此，越来越多的国家和能源公司正在投资于更可持续的能源，特别是太阳能，预计太阳能在可再生能源组合中将越来越普遍，但由于云层稀疏造成发电不连续，太阳能电池板发电的变化很大。这就限制了太阳能的全面发展。为了解决这个问题，我们希望预测辐照度（太阳能通量），以及更广泛的电力生产，从几分钟到几个月。这将改善以下方面：工厂和电网运营、供电质量、电网规划、网络平衡、生产优化和电力交易。考虑到这一点，（180张由地面半球相机拍摄的天空图像）是在20分钟时间窗内进行局部短期辐照度预报的一个有希望的起点，可以采用不同的方法来根据以固定时间间隔拍摄的这些图像的序列来预测辐照度。一种现有的方法是在每个时间步从天空图像生成云层的3D模型，并从观测到的位移估计未来的变化。然后利用物理模型从预测的地面阴影中近似计算给定点的辐照度分量。然而，这种手工制作的方法确实涉及一些影响预测质量的近似值。主要问题是难以将云结构和云移动的复杂性集成到模型中。我们最初采用的方法涉及应用最先进的机器学习技术来解决这一预测问题。卷积神经网络（CNN）将提供对场景的理解，它已被证明是计算机视觉中非常有效的工具，用于从不同抽象层次的图像中提取相关特征。预测步骤将通过递归神经网络（RNN）实现，该网络使模型能够从序列中提取额外的信息，即为模型添加记忆。我们的想法也是通过MC-Dropout或高斯过程等方式量化这些深度神经网络的预测不确定性。最近，一种类似的方法已成功地用于根据卫星图像预测雨林状况，将通过使用统计学和机器学习领域的其他工具以及利用气象数据或卫星图像等其他数据，进一步提高该模型的预测能力，以提高其时间和空间有效性。

The pressing need to reduce global emissions in the next decades is forcing countries to strive for a more sustainable model of development. Besides introducing necessary demand-side mitigation options, international panels recommend that governments carry out a deep energy transition, leading to a gradual decarbonisation of the supply-side. For this reason, a growing number of countries and energy companies are investing in more sustainable sources of energy, and in particular in solar energy, which is expected to become increasingly prevalent in renewable energy mixes.The production of electricity from solar panels suffers, however, from high variability due to the discontinuity of the energy generation caused by sparse cloud cover. This currently limits the full development of solar energy. To address this problem, we would like to forecast irradiance (solar flux), and more broadly electricity production, from a few minutes to several months ahead. This would improve the following: plant and grid operations, quality of the power supply, grid planning, network balance, production optimisation and electricity trading.With this in mind, the analysis of ground-based images (180 sky images taken by hemispherical cameras on the ground) is a promising starting point for local short-term irradiance forecasts in a 20 minutes time window, over an area imposed by the skyline.Different approaches can be taken to such forecasting of irradiance from sequences of these images taken at fixed time intervals. One existing method is to generate, at each time step, a 3D model of the cloud cover from sky images and estimate future changes from observed displacements. Physical models are then exploited to approximate irradiance components at a given point from the predicted shadow on the ground.This hand-crafted approach does, however, involve some approximations which undermine the quality of the forecast. The main issue being the difficulty of integrating the complexity of cloud structure and cloud movement into models.The approach we will initially adopt involves the application of state-of-the-art Machine Learning techniques to tackle this forecasting problem. The understanding of the scene will be provided by Convolutional Neural Networks (CNN), which have proved to be a very efficient tool in computer vision for extracting relevant features from images at different levels of abstraction. The prediction step will be achieved by a Recurrent Neural Network (RNN), which enables the model to extract additional information from sequences, i.e. adds memory to the model. The idea is also to quantify forecast uncertainties from these deep neural networks through, for example, MC-Dropout or Gaussian Processes. Recently, a similar approach was successfully applied to forecasting rain forest conditions from satellite images.The forecasting power of the model will be further improved to increase its temporal and spatial validity by using other tools from the fields of Statistics and Machine Learning and by exploiting additional data such as meteorological data or satellites images.