论文标题
卷积神经网络应用于天空图像，以预测短期太阳辐照度
Convolutional Neural Networks applied to sky images for short-term solar irradiance forecasting
论文作者
论文摘要
尽管太阳能领域取得了进步，但太阳预测技术的改进，解决间歇性的电力生产方面，对于确保其未来的整合到更广泛的能源供应方面仍然至关重要。一种有希望的预测辐照度变化的方法包括对地面或卫星图像的云覆盖动态进行建模。这项工作提出了有关使用半球形天空图像和外源变量的2至20分钟辐照度预测的深卷卷神经网络的初步结果。我们在8个月内以2分钟的时间分辨率评估了一组辐照度测量和相应的天空图像的模型。为了概述在短期辐照度预测的背景下学习神经网络的学习，我们实施了可视化技术，揭示了Sky Images中训练有素的算法识别的模式类型。此外，我们表明，相对于基于卑鄙的平方误差的智能持久性模型，在同一天使用过去样本的训练模型提高了他们的预测技能，在预测的10分钟内预测约为10％。这些结果强调了在短期预测中整合以前的当天数据的好处。反过来，这可以通过模型微调或使用复发单元来实现，以促进从过去数据中提取相关的时间特征。
Despite the advances in the field of solar energy, improvements of solar forecasting techniques, addressing the intermittent electricity production, remain essential for securing its future integration into a wider energy supply. A promising approach to anticipate irradiance changes consists of modeling the cloud cover dynamics from ground taken or satellite images. This work presents preliminary results on the application of deep Convolutional Neural Networks for 2 to 20 min irradiance forecasting using hemispherical sky images and exogenous variables. We evaluate the models on a set of irradiance measurements and corresponding sky images collected in Palaiseau (France) over 8 months with a temporal resolution of 2 min. To outline the learning of neural networks in the context of short-term irradiance forecasting, we implemented visualisation techniques revealing the types of patterns recognised by trained algorithms in sky images. In addition, we show that training models with past samples of the same day improves their forecast skill, relative to the smart persistence model based on the Mean Square Error, by around 10% on a 10 min ahead prediction. These results emphasise the benefit of integrating previous same-day data in short-term forecasting. This, in turn, can be achieved through model fine tuning or using recurrent units to facilitate the extraction of relevant temporal features from past data.
