School of Electrical Engineering, Southeast University, Nanjing 210018, China.
Jiangsu Provincial Key Laboratory of Smart Grid Technology and Equipment, Nanjing 210018, China.
Sensors (Basel). 2021 Nov 21;21(22):7750. doi: 10.3390/s21227750.
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.
作为家庭电器能源使用监测的虚拟传感器网络，非侵入式负载监测正在成为精细化电力分析和家庭能源管理的技术基础。为了实现稳健可靠的监测，集成方法在负载分解中备受期待，但设计难度和计算效率的障碍仍然存在。针对这一问题，本文提出了一种集成多异质性的集成设计，用于非侵入式能源使用分解。首先，提出了利用异构设计的思想，并建立了相应的负载分解集成框架。然后，为各个分类器分配稀疏编码模型，并通过引入不同的距离和相似性度量来丰富组合分类器，而无需考虑稀疏性，从而形成相互异质的分类器。最后，基于多次评估的决策过程在多异质委员会的交互作用下进行微调，并最终作为决策者进行部署。通过对低压网络模拟器和现场测量数据集的验证，所提出的方法在稳健性方面有效地提高了负载分解性能。通过适当将异构设计引入集成方法，可以在降低计算负担的同时提高负载监测的性能，这激发了研究人员的研究热情，以探索实用的非侵入式负载监测实现的有效集成策略。
