Neural networks-based learning of the distribution of non-dispatchable renewable electricity generation from sources such as photovoltaics (PV) and wind as well as load demands has recently gained attention. Normalizing flow density models are particularly well suited for this task due to the training through direct log-likelihood maximization. However, research from the field of image generation has shown that standard normalizing flows can only learn smeared-out versions of manifold distributions. Previous works on normalizing flow-based scenario generation do not address this issue, and the smeared-out distributions result in the sampling of noisy time series. In this paper, we exploit the isometry of the principal component analysis (PCA), which sets up the normalizing flow in a lower-dimensional space while maintaining the direct and computationally efficient likelihood maximization. We train the resulting principal component flow (PCF) on data of PV and wind power generation as well as load demand in Germany in the years 2013 to 2015. The results of this investigation show that the PCF preserves critical features of the original distributions, such as the probability density and frequency behavior of the time series. The application of the PCF is, however, not limited to renewable power generation but rather extends to any data set, time series, or otherwise, which can be efficiently reduced using PCA.
翻译:以神经网络为基础,从光伏、风和负载需求等来源对不可分配的可再生发电的分布进行神经网络学习,最近引起了人们的注意。由于通过直接对日志进行最大化培训,流密度模型的标准化对于这项任务特别适合。然而,图像生成领域的研究表明,标准化流流的标准化只能从多种分布中学习被抹掉的版本。以往关于流基情景生成正常化的工作没有解决这个问题,被抹掉的分布导致抽取时间序列。在本文件中,我们利用主要组成部分分析(PCA)的偏差,该分析在低维度空间建立正常流,同时保持直接和计算高效的可能性最大化。我们在2013至2015年对德国的光电和风能生成数据以及负载需求进行了培训。本次调查的结果显示,PCFCF保存了原始分布的关键特征,如时间序列的概率密度和频率行为。然而,使用可再生能源数据序列的应用可能缩小,但使用可再生能源生成的时间序列则可能降低。