基于深度卷积自编码器的住宅负荷分布聚类

Seunghyoung Ryu, Hyungeun Choi, Hyoseop Lee, Hongseok Kim, V. Wong
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引用次数: 14

摘要

在能源数据分析中,负荷分布聚类对于各种智能电网应用(如需求响应、负荷预测和电价设计)至关重要。传统的聚类技术大多是基于某一时间段内具有代表性的时域负荷分布,不能很好地捕捉到负荷的日变化和季节变化。在本文中,我们提出了一个基于深度学习的客户负载概况聚类框架,该框架可以共同捕获日常和季节变化。通过利用卷积自编码器(CAE),将时间域中的年负荷曲线转换为较小维编码空间中的代表性向量。然后根据CAE编码的向量确定集群。我们将提出的框架应用于1,405户家庭的年负荷概况,并验证经过培训的CAE可以将这些负荷概况编码到大约100倍的小维度空间中。编码的负载曲线可以被CAE解码,损耗在1-3%之间可以忽略不计。聚类负载图像可以显示每日和季节变化,并且在编码空间中聚类将聚类过程加快了近三个数量级。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Residential Load Profile Clustering via Deep Convolutional Autoencoder
In energy data analytics, load profile clustering is essential for various smart grid applications such as demand response, load forecasting, and tariff design. Most of the conventional clustering techniques are based on a representative time domain load profile within a certain period, and the daily and seasonal variations are not well captured. In this paper, we propose a deep learning based customer load profile clustering framework that jointly captures daily and seasonal variations. By leveraging convolutional autoencoder (CAE), the yearly load profile in the time domain is converted into a representative vector in the smaller dimensional encoded space. The clusters are then determined based on the vectors encoded by the CAE. We apply the proposed framework to 1,405 households' yearly load profiles and verify that the trained CAE can encode those load profiles into approximately 100 times smaller dimensional space. The encoded load profiles can be decoded by the CAE with a negligible loss between 1–3%. The clustered load images can visualize both daily and seasonal variations, and clustering in the encoded space speeds up the clustering process by almost three orders of magnitude.
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