An Improved Sequence-to-Point Learning for Non-Intrusive Load Monitoring Based on Discrete Wavelet Transform

Chen, Xiong; Cai, Zhaohui; Liu, Shubo; Ji, Luo; Tu, Guoqing

doi:10.1109/tim.2023.3296127

Cited by 5 publications

(1 citation statement)

References 32 publications

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“…Seq2point learning is first proposed with a CNN in [ 14 ] as a method of NILM, where the input is a window of the total energy data and the output is a single point of the target appliance. Further, different algorithms are incorporated into seq2point models for energy disaggregation to improve the identification accuracy, for example, the temporal convolutional network (TCN) [ 15 ], the bidirectional dilated residual network [ 4 ], the discrete wavelet transform [ 16 ], and the bi-directional TCN [ 17 ]. Different from the seq2point model, the seq2seq-based NILM aims to output the sequences, of an equal length to the input, that contain only the power consumption of a single appliance.…”

Section: Introductionmentioning

confidence: 99%

Non-Intrusive Load Identification Based on Retrainable Siamese Network

Lu,

Kang,

Meng

et al. 2024

Sensors

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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 unknown load identification can be achieved by updating the Siamese network in real time. The public WHITED and PLAID datasets are used for the validation of the proposed method. Finally, the practicality and scalability of the method are demonstrated using a real-house environment test to prove the ability of online retraining on an embedded Linux system with STM32MP1 as the core.

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