Sequence-to-Sequence-Based Beta-VAE Combined With IECA Attention Mechanism for Energy Disaggregation Algorithm

Yu, Haimeng; Pang, Chengxin; Xuan, Yang; Chen, Yongbo; Zeng, Xinhua

doi:10.1109/tim.2023.3325867

Cited by 6 publications

(1 citation statement)

References 30 publications

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“…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. In [ 18 , 19 ], LSTM and an autoencoder combined with an attention mechanism are employed for seq2seq load aggregation models, respectively. These approaches rely on calculation and processing using intelligent algorithms; thus, they generally require a large amount of computation resources.…”

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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Section: Introductionmentioning

confidence: 99%

Non-Intrusive Load Identification Based on Retrainable Siamese Network

Lu,

Kang,

Meng

et al. 2024

Sensors

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A novel fuzzy extended state observer-based control for electro-optical tracking system with disturbances and measurement noises: Design, analysis and experiments

Wang,

Zhang,

Mao

et al. 2024

Control Engineering Practice

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Load Energy Decomposition Algorithm Based on Improved Bidirectional Transformer Combined With Time-Sensing Self-Attention

Xuan,

Pang,

et al. 2024

IEEE Access

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Non-Intrusive Load Monitoring (NILM) is a technique capable of extracting the quantity, type and operational status from overall power data in a specific area. Through energy disaggregation, the total power consumption can be decomposed into device-level power consumption. In recent years, with the advancement of deep learning techniques, numerous improvements have been made in energy disaggregation technologies. However, for NILM tasks, significant variations in device switching can result in data imbalance, and devices in different operating states can impact the model's prediction accuracy. To address these issues, this paper proposes a method based on Bidirectional Transformer and GRU, combined with time-aware self-attention for load prediction(CTA-BERT). In this model, We leverage the bidirectional transformer of BERT to learn features at different positions in the power sequence. Simultaneously, GRU is added to capture long-term sequence dependencies. Device time-state variables and dilated convolutional layers are introduced in the Transformer section to effectively capture long sequence dependencies and global features. We also enhance the masking strategy to adapt the appliances in different states. Finally, on the REDD and UK-DALE datasets, we tested and compared the proposed method with several state-ofthe-art NILM algorithms. The results indicate that the model's average absolute error (MAE) is reduced by an average of 25% across all devices, F1-Score is improved by approximately 20%, and prediction accuracy is significantly enhanced.

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Sequence-to-Sequence-Based Beta-VAE Combined With IECA Attention Mechanism for Energy Disaggregation Algorithm

Cited by 6 publications

References 30 publications

Non-Intrusive Load Identification Based on Retrainable Siamese Network

Non-Intrusive Load Identification Based on Retrainable Siamese Network

A novel fuzzy extended state observer-based control for electro-optical tracking system with disturbances and measurement noises: Design, analysis and experiments

Load Energy Decomposition Algorithm Based on Improved Bidirectional Transformer Combined With Time-Sensing Self-Attention

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