In this paper, we investigate the benefits of time-series imaging in load disaggregation, as we augment the wide-spread sequence–to–sequence approach by a key element: an imaging block.
The approach presented in this paper converts an input sequence to an image, which in turn serves as input to a modified version of a common Denoising Autoencoder architecture used in load disaggregation. Based on these input images, the Autoencoder estimates the power consumption of a particular appliance. The main contribution presented in this paper is a comparison study between three common imaging techniques: Gramian Angular Fields, Markov Transition Fields, and Recurrence Plots. Further, we assess the performance of our augmented networks by a comparison with two benchmarking implementations, one based on Markov Models and the other one being a common Denoising Autoencoder. The outcome of our study reveals that in 19 of 24 cases, the considered augmentation techniques provide improved performance over the baseline implementation. Further, the findings presented in this paper indicate that the Gramian Angular Field could be better suited, though the Recurrence Plot was observed to be a viable alternative in some cases.
Index Terms— time-series imaging, load disaggregation, NILM, neural networks