Toward robust non‐intrusive load monitoring via probability model framed ensemble method

Abstract

As a pivotal technological foundation for smart home implementation, non‐intrusive load monitoring is emerging as a widely recognized and popular technology to replace the sensors or sockets networks for the detailed household appliance monitoring. In this paper, a probability model framed ensemble method is proposed for the target of robust appliance monitoring. Firstly, the non‐intrusive load disaggregation‐oriented ensemble architecture is presented. Then, dictionary learning model is utilized to formulate the individual classifier, while the sparse coding‐based approach is capable of providing multiple solutions under greedy mechanism. Furthermore, a fully probabilistic model is established for combined classifier, where the candidate solutions are all la-belled with probability scores and evaluated via two‐stage decision‐making. The proposed method is tested on both low‐voltage network simulator platform and field measurement datasets, and the results show that the proposed ensemble method always guarantees an enhancement on the performance of non‐intrusive load disaggregation. Besides, the proposed approach shows high flexibility and scalability in classification model selection. Therefore, by initializing the architecture and approach of ensemble method‐based NILM, this work plays a pioneer role in using ensemble method to improve the robustness and reliability of non‐intrusive appliance monitoring.