Photovoltaic (PV) is receiving increasing attention due to its sustainability and low carbon footprint. However, the penetration level of PV is still relatively low because of its intermittency. This uncertainty can be handled by accurate PV forecasting, which requires high-quality solar data. Nevertheless, up to 40% of solar data can be found missing, which significantly worsens the quality of solar data. This letter proposes a novel solarGAN method for multivariate solar data imputation, in which necessary modifications are made on the input of generative adversarial network (GAN) to effectively tackle the relatively independent solar time series data. Case studies on a public dataset show that the proposed solarGAN outperforms several commonly-used machine learning and GAN based data imputation methods with at least 23.9% reduction of mean squared error.

中文翻译：

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SolarGAN：使用生成对抗网络进行多元太阳数据估算

光伏（PV）由于其可持续性和低碳足迹而受到越来越多的关注。但是，由于PV的间歇性，其渗透水平仍然相对较低。这种不确定性可以通过准确的PV预测来处理，这需要高质量的太阳能数据。尽管如此，仍然可以找到多达40％的太阳能数据丢失，这严重恶化了太阳能数据的质量。这封信提出了一种用于多变量太阳数据估算的新型solarGAN方法，其中对生成的对抗网络（GAN）的输入进行了必要的修改，以有效处理相对独立的太阳时间序列数据。在公共数据集上的案例研究表明，拟议的solarGAN至少在23种方面优于几种常用的机器学习和基于GAN的数据插补方法。