Principal component density estimation for scenario generation using normalizing flows

IF 2.4 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Eike Cramer, A. Mitsos, R. Tempone, M. Dahmen
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引用次数: 9

Abstract

Abstract 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 2013–2015. 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 dataset, time series, or otherwise, which can be efficiently reduced using PCA.
使用归一化流的场景生成的主成分密度估计
摘要基于神经网络的学习最近引起了人们的关注,学习来自光伏(PV)和风能等来源的不可调度可再生发电的分布以及负载需求。由于通过直接对数似然最大化进行训练,归一化流密度模型特别适合此任务。然而,来自图像生成领域的研究表明,标准归一化流只能学习流形分布的涂抹版本。先前关于规范化基于流的场景生成的工作没有解决这个问题,并且模糊分布导致了噪声时间序列的采样。在本文中,我们利用了主成分分析(PCA)的等距性,它在低维空间中建立了归一化流,同时保持了直接和计算有效的似然最大化。我们根据德国2013-2015年的光伏和风力发电数据以及负荷需求对由此产生的主成分流(PCF)进行了训练。研究结果表明,PCF保留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,PCF的应用并不局限于可再生能源发电,而是扩展到任何数据集、时间序列或其他方面,这些数据集、时序或其他方面可以使用PCA有效地减少。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
DataCentric Engineering
DataCentric Engineering Engineering-General Engineering
CiteScore
5.60
自引率
0.00%
发文量
26
审稿时长
12 weeks
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