Sensors, Vol. 24, Pages 2562: Non-Intrusive Load Identification Based on Retrainable Siamese Network

Sensors, Vol. 24, Pages 2562: Non-Intrusive Load Identification Based on Retrainable Siamese Network Sensors doi: 10.3390/s24082562 Authors: Lingxia Lu Ju-Song Kang Fanju Meng Miao Yu Non-intrusive load monitoring (NILM) can identify each electrical load and its operating state in a household by using the voltage and current data measured at a single point on the bus, thereby behaving as a key technology for smart grid construction and effective energy consumption. The existing NILM methods mainly focus on the identification of pre-trained loads, which can achieve high identification accuracy and satisfying outcomes. However, unknown load identification is rarely involved among those methods and the scalability of NILM is still a crucial problem at the current stage. In light of this, we have proposed a non-intrusive load identification method based on a Siamese network, which can be retrained after the detection of an unknown load to increase the identification accuracy for unknown loads. The proposed Siamese network comprises a fixed convolutional neural network (CNN) and two retrainable back propagation (BP) networks. When an unknown load is detected, the low-dimensional features of its voltage–current (V-I) trajectory are extracted by using the fixed CNN model, and the BP networks are retrained online. The finetuning of BP network parameters through retraining can improve the representation ability of the network model; thus, a high accuracy of...
Source: Sensors - Category: Biotechnology Authors: Tags: Article Source Type: research