Toward Robust Non-Intrusive Load Monitoring via Probability Model Framed Ensemble Method

Toward Robust Non-Intrusive Load Monitoring via Probability Model Framed Ensemble Method

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As a pivotal read more 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 labelled 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, sensationnel kiyari this work plays a pioneer role in using ensemble method to improve the robustness and reliability of non-intrusive appliance monitoring.