Acting as a virtual sensor network for household appliance energy use monitoring, non-intrusive load monitoring is emerging as the technical basis for refined electricity analysis as well as home energy management. Aiming for robust and reliable monitoring, the ensemble approach has been expected in load disaggregation, but the obstacles of design difficulty and computational inefficiency still exist. To address this, an ensemble design integrated with multi-heterogeneity is proposed for non-intrusive energy use disaggregation in this paper. Firstly, the idea of utilizing a heterogeneous design is presented, and the corresponding ensemble framework for load disaggregation is established. Then, a sparse coding model is allocated for individual classifiers, and the combined classifier is diversified by introducing different distance and similarity measures without consideration of sparsity, forming mutually heterogeneous classifiers. Lastly, a multiple-evaluations-based decision process is fine-tuned following the interactions of multi-heterogeneous committees, and finally deployed as the decision maker. Through verifications on both a low-voltage network simulator and a field measurement dataset, the proposed approach is demonstrated to be effective in enhancing load disaggregation performance robustly. By appropriately introducing the heterogeneous design into the ensemble approach, load monitoring improvements are observed with reduced computational burden, which stimulates research enthusiasm in investigating valid ensemble strategies for practical non-intrusive load monitoring implementations.
Keywords: artificial intelligence; energy disaggregation; ensemble method; heterogeneous design; non-intrusive load monitoring.