A robust non-intrusive load disaggregation method with roof-top photovoltaics

Abstract

As a promising technology dealing with electric behavior analysis and energy characteristic mining, non-intrusive load monitoring is popular with these people with energy conservation awareness. Coincidentally, these residents are with strong motivation to install roof-top photovoltaics. Therefore, it is a real challenge to implement non-intrusive load disaggregation considering PV integrations, which is rarely researched. In this paper, this problem is thoroughly investigated with an achievement of a corresponding robust load disaggregation approach. At the first stage, the problem formulation is established based on the evolved dictionary learning scheme, where the special features of PV, e.g., no switching pattern and continuous power variation, are addressed. Secondly, aiming at robustness of monitoring, the steady state disaggregation is enhanced by integrating with event-based detection, forming a hybrid validation strategy. Then, the whole problem is solved following the sparse coding principle. The proposed study is verified by both simulation tests and field measurements, and the results show that the proposed method is accurate and robust in non-intrusive load monitoring with PV consideration. Besides, it is compatible with various load signature utilization, leading to a practical disaggregation solution.