Department of Electrical Engineering, Ming Chi University of Technology, New Taipei City 24301, Taiwan.
Sensors (Basel). 2020 Mar 16;20(6):1649. doi: 10.3390/s20061649.
Non-intrusive load monitoring (NILM) is a cost-effective approach that electrical appliances are identified from aggregated whole-field electrical signals, according to their extracted electrical characteristics, with no need to intrusively deploy smart power meters (power plugs) installed for individual monitored electrical appliances in a practical field of interest. This work addresses NILM by a parallel Genetic Algorithm (GA)-embodied Artificial Neural Network (ANN) for Demand-Side Management (DSM) in a smart home. An ANN's performance in terms of classification accuracy depends on its training algorithm. Additionally, training an ANN/deep NN learning from massive training samples is extremely computationally intensive. Therefore, in this work, a parallel GA has been conducted and used to integrate meta-heuristics (evolutionary computing) with an ANN (neurocomputing) considering its evolution in a parallel execution relating to load disaggregation in a Home Energy Management System (HEMS) deployed in a real residential field. The parallel GA that involves iterations to excessively cost its execution time for evolving an ANN learning model from massive training samples to NILM in the HEMS and works in a divide-and-conquer manner that can exploit massively parallel computing for evolving an ANN and, thus, reduce execution time drastically. This work confirms the feasibility and effectiveness of the parallel GA-embodied ANN applied to NILM in the HEMS for DSM.
非侵入式负载监测（NILM）是一种经济有效的方法，可以根据提取的电气特性，从聚合的全领域电气信号中识别电器，而无需在实际感兴趣的领域中侵入性地部署用于单个监测电器的智能电表（电源插头）。这项工作通过并行遗传算法（GA）体现的人工神经网络（ANN）来解决智能家居中的需求侧管理（DSM）中的 NILM 问题。ANN 的分类准确性取决于其训练算法。此外，从大量训练样本中训练 ANN/深度学习网络的计算量非常大。因此，在这项工作中，已经进行了并行 GA 并将其用于将启发式算法（进化计算）与 ANN（神经计算）集成在一起，考虑到其在与家庭能源管理系统（HEMS）中的负载分解相关的并行执行中的进化，该系统部署在实际住宅领域中。并行 GA 涉及迭代，以从大量训练样本中过度消耗其执行时间，以便在 HEMS 中进行 NILM 的 ANN 学习模型进化，并以分而治之的方式工作，可以利用大规模并行计算来进化 ANN，从而大大缩短执行时间。这项工作证实了并行 GA 体现的 ANN 应用于 HEMS 中用于 DSM 的 NILM 的可行性和有效性。
