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arXiv · 1802.02139

On the Feasibility of Generic Deep Disaggregation for Single-Load Extraction

Karim Said Barsim·Bin Yang
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Abstract

Recently, and with the growing development of big energy datasets, data-driven learning techniques began to represent a potential solution to the energy disaggregation problem outperforming engineered and hand-crafted models. However, most proposed deep disaggregation models are load-dependent in the sense that either expert knowledge or a hyper-parameter optimization stage is required prior to training and deployment (normally for each load category) even upon acquisition and cleansing of aggregate and sub-metered data. In this paper, we present a feasibility study on the development of a generic disaggregation model based on data-driven learning. Specifically, we present a generic deep disaggregation model capable of achieving state-of-art performance in load monitoring for a variety of load categories. The developed model is evaluated on the publicly available UK-DALE dataset with a moderately low sampling frequency and various domestic loads.

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Karim Said Barsim, Bin Yang. 2018-02-05. On the Feasibility of Generic Deep Disaggregation for Single-Load Extraction. https://arxiv.org/abs/1802.02139

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