On the Feasibility of Generic Deep Disaggregation for Single-Load Extraction
This addresses the need for more adaptable and efficient energy disaggregation in smart grid applications, though it appears incremental as it builds on existing data-driven techniques.
The paper tackles the problem of load-dependent energy disaggregation models requiring expert knowledge or hyper-parameter tuning by proposing a generic deep disaggregation model, which achieves state-of-the-art performance on the UK-DALE dataset for various domestic loads.
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.