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.