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. is the process of inferring individual load profiles at the end-use level from a single or a limited number of sensing points. Promising applications of disaggregated data have motivated a growing research community to reach a widely acceptable and scalable solution. Energy disaggregation proved to be a challenging source separation problem in which a considerably large number of parameters are to be estimated from a limited set of measurements with little constraints. In the last decade, energy disaggregation has witnessed an unprecedented wide-spreading research which is easily observed from the wide variety of learning techniques applied to this problem alongside with the growing number of energy datasets developed specifically for this research field. More recently, and analogous to the current breakthrough in datadriven learning, deep neural networks have re-gained their interest in addressing the energy disaggregation problem, especially alongside with the recently developed large energy datasets required for training such complex models [1] [2] [3] [4] [5] [6] [7] [8] . The progress in this trend, however, is relatively slow when compared to the development in either field separately. This is sometimes attributed to the high risk of over-fitting in neural network models [9] , insufficiency or low diversity of publicly available energy datasets [4] , or limited insights and understanding of the learning behavior of these models [5] . In this paper, we first present a feasibility study on the development of a generic data-driven model suitable for enduse load monitoring. The proposed disaggregation model exploits a fully convolutional neural network architecture and is generic in the sense that none of the model hyperparameters is dependent on the load category. We assess the feasibility of such a model through empirical evaluation of the monitoring performance across various load categories in a publicly available energy dataset.
Barsim et al. developed a generic disaggregation model based on data-driven learning REF .
On the Feasibility of Generic Deep Disaggregation for Single-Load Extraction
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