论文标题
使用条件变异自动编码器生成上下文负载配置文件
Generating Contextual Load Profiles Using a Conditional Variational Autoencoder
论文作者
论文摘要
生成与历史数据具有相似分布和依赖性的电力系统对于系统规划和安全评估的任务至关重要的，尤其是在历史数据不足的情况下。在本文中，我们根据条件变化自动编码器（CVAE）神经网络体系结构描述了一种工业和商业客户负载概况的生成模型，由于此类配置文件的高度可变性质，该模型具有挑战性。生成的上下文负载概况在一年中的月份进行了调节，并与电网进行了典型的电力交换。此外，世代的质量在视觉和统计上都经过评估。实验结果表明，我们提出的CVAE模型可以捕获历史负载谱的时间特征，并以满意的单变量分布和多元依赖性生成“现实”数据。
Generating power system states that have similar distribution and dependency to the historical ones is essential for the tasks of system planning and security assessment, especially when the historical data is insufficient. In this paper, we described a generative model for load profiles of industrial and commercial customers, based on the conditional variational autoencoder (CVAE) neural network architecture, which is challenging due to the highly variable nature of such profiles. Generated contextual load profiles were conditioned on the month of the year and typical power exchange with the grid. Moreover, the quality of generations was both visually and statistically evaluated. The experimental results demonstrate our proposed CVAE model can capture temporal features of historical load profiles and generate `realistic' data with satisfying univariate distributions and multivariate dependencies.
