A Deep Cascade Framework for Non-Intrusive Power Disaggregation in Solar-Powered Households

Inverter-Based Resources are commonly installed behind the customer meters. Thus, non-intrusive power monitoring systems must handle power signals of different natures measured at the main meter and estimate power generation to ensure the observability of the power grid. This paper proposes a non-intrusive disaggregation approach that includes photovoltaic power production with load monitoring. The approach is based on an innovative cascade learning framework that exploits the solar power estimate to simplify the load monitoring task, thereby improving the overall disaggregation performance. Compared to five state-of-the-art models, our method achieves the lowest disaggregation error on two different real-world public datasets, with improvements of 20.86% and 8.67% over the runner-up benchmark. The code to reproduce the method is available on GitHub<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>.