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

TL;DR

This paper investigates the potential of a universal deep learning model for energy disaggregation that can perform well across different load types without load-specific tuning, using the UK-DALE dataset.

Contribution

The paper introduces a generic deep disaggregation model that achieves state-of-the-art performance across multiple load categories without load-dependent customization.

Findings

Achieves competitive performance on UK-DALE dataset

Demonstrates feasibility of load-agnostic disaggregation models

Reduces need for load-specific hyper-parameter tuning

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.