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 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, this work plays a pioneer role in using ensemble method to improve the robustness and reliability of non-intrusive appliance monitoring.

An Enhanced Ensemble Approach for Non-Intrusive Energy Use Monitoring Based on Multidimensional Heterogeneity

“…Since the individual classifier is established based on the sparse coding approach, the conventional dictionary learning approach is compared, and denoted as CDA [32]. In addition, the former proposal of ensemble-method-based NILM in [36], framed by the probability model, is also compared and denoted as EPA. Besides, in order to investigate the insights of our proposed approach, the detailed performance of individual classifiers is also recorded and analyzed.…”

“…Besides, in order to investigate the insights of our proposed approach, the detailed performance of individual classifiers is also recorded and analyzed. In this subsection, the individual classifiers are formed based on the feature selection bagging strategy [36], and denoted as ICA1, ICA2, ICA3, and ICA4, respectively.…”

“…The proposed heterogeneity-enhanced ensemble approach is still denoted as PHA in this subsection, as well as the compared approaches by conventional methods, i.e., CDA [32] and EPA [36]. Since the data of House 1 are low-frequency without harmonics information, the individual classifiers are generated based on the original bootstrap sampling strategy and, respectively, denoted as ICA1, ICA2, ICA3, and ICA4 in this subsection.…”

“…Both of the above works focus on event-based load monitoring. Our team has established a general ensemble framework based on bagging in [36] for the load disaggregation of steady-state data, and proved its performance robustness and model flexibility in diverse NILM scenarios.…”

“…However, to our knowledge, the current ensemble strategies applied to NILM all follow the evaluation criterion used in individual classifiers, even the probabilistic quantitative scoring method proposed in [36]. Such implementation requires the individual classifier to be reliable and differentiated, but the bias can hardly be avoided since the combined classifier and individual classifiers are homogeneous.…”