Non‐intrusive load monitoring by voltage–current trajectory enabled asymmetric deep supervised hashing

Han, Yinghua; Xu, Yulong; Huo, Yaxin; Zhao, Qiang

doi:10.1049/gtd2.12242

Cited by 10 publications

(5 citation statements)

References 41 publications

(57 reference statements)

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“…To verify the effectiveness of the model, we compared the load identification results with state-of-the-art algorithms. Ref Han et al (2021) is a V-I trajectory enabled asymmetric deep supervised hashing (ADSH) method, and De Baets et al ( 2018) is a load identification method based on convolutional neural network. To verify the identification of the model.…”

Section: Experiments Results and Discussionmentioning

confidence: 99%

Similarity preserving hashing for appliance identification based on V-I trajectory

Liu,

Zheng

et al. 2023

Front. Energy Effic.

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Non-intrusive load monitoring (NILM) is a technique used to monitor energy consumption in buildings without requiring hardware installation on individual appliances. This approach offers a cost-effective and scalable solution to enhance energy efficiency and reduce energy usage. Recent advancements in NILM primarily employ deep-learning algorithms for appliance identification. However, the substantial number of parameters in deep learning models presents challenges in quickly and effectively identifying appliances. An effective technique for appliance identification is analyzing the appliances’ voltage-current (V-I) trajectory signature. This research introduces a novel hashing method that learns compact binary codes to achieve highly efficient appliance V-I trajectory identification. Specifically, this paper uses a profound structure to acquire V-I trajectory image features by acquiring multi-level non-linear transformations. Subsequently, we merge these intermediary traits with high-level visual data from the uppermost layer to carry out the V-I trajectory image retrieval process. These condensed codes are subjected to three distinct standards: minimal loss in quantization, uniformly distributed binary components, and autonomous bits that are not interdependent. As a result, the network easily encodes newly acquired query V-I images for appliance identification by propagating them through the network and quantizing the network’s outputs into binary code representations. Through extensive experiments conducted on the PLAID dataset, we demonstrate the promising performance of our approach compared to state-of-the-art methods.

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Section: Experiments Results and Discussionmentioning

confidence: 99%

Similarity preserving hashing for appliance identification based on V-I trajectory

Liu,

Zheng

et al. 2023

Front. Energy Effic.

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show abstract

“…It can ensure the accuracy of the known appliance recognition while realizing the unknown appliance recognition. Yinghua Han et al [37] proposed an asymmetric deep supervised hashing (ADSH) method based on the V-I trajectory signatures for NILM. This method used the V-I trajectory as the input for model, which solved the problems of the low calculation efficiency of massive data and low discrimination of manually extracted signatures.…”

Section: Appliance Featuresmentioning

confidence: 99%

An Overview of Non-Intrusive Load Monitoring Based on V-I Trajectory Signature

Zhao

Liu

et al. 2023

Energies

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Non-intrusive load monitoring (NILM) can obtain fine-grained electricity consumption information of each appliance by analyzing the voltage and current data measured at a single point on the bus, which is of great significance for promoting and improving the efficiency and sustainability of the power grid and enhancing the energy efficiency of users. NILM mainly includes data collection and preprocessing, event detection, feature extraction, and appliance identification. One of the most critical steps in NILM is signature extraction, which is the basis for all algorithms to achieve good state detection and energy disaggregation. With the generalization of machine learning algorithms, different algorithms have also been used to extract unique signatures of appliances. Recently, the development and deployment of the voltage–current (V-I) trajectory signatures applied for appliance identification motivated us to present a comprehensive review in this domain. The V-I trajectory signatures have the potential to be an intermediate domain between computer vision and NILM. By identifying the V-I trajectory, we can detect the operating state of the appliance. We also summarize existing papers based on V-I trajectories and look forward to future research directions that help to promote the field’s development.

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“…In practice, the basis of load disaggregation is the efficacious selection of load signatures that can tell the distinction between different categories. The most common ones are transient and steady-state [17]. Transient signatures, such as high-frequency changes of total power and current [19], are more likely to be selected for low-voltage residential loads with frequent switches of working state [20].…”

Section: Introductionmentioning

confidence: 99%

Simulation‐data‐driven load disaggregation based on multi‐channel neural network for industrial and commercial users

Yang

Jiang

Sun

et al. 2023

IET Generation Trans & Dist

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The immeasurable energy-saving potential of industrial parks and commercial buildings with unprecedented growth in scale and quantity can be well exploited by load disaggregation, a promising technique for demand-side refinement management. However, traditional load disaggregation studies mainly focus on residential users and neglect the collection and analysis of industrial and commercial loads. This study employs a simulated response model of versatile load categories with voltage-dependent load functions to overcome the unavailability of industrial and commercial datasets. Specially, a weighting factors allocation method considering probability statistics of power consumption distribution characteristics is proposed to construct a typical daily multi-type load aggregation model. Furthermore, an intelligent load disaggregation strategy is developed based on a multi-channel neural network to constructively improve multi-feature tracking and convergence efficiency. Finally, the simulation results of the detailed study case confirm the tractability and effectiveness of the proposed approach to carry out load identification among multi-type users, with an accuracy of 94.9% and errors of individual categories controlled between 3.32% and 10.686%. Moreover, additional interference tests demonstrate that the developed model has superiority over common load disaggregation structures, with better tolerance to exceptional voltage fluctuation and noise.

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Non‐intrusive load monitoring by voltage–current trajectory enabled asymmetric deep supervised hashing

Cited by 10 publications

References 41 publications

Similarity preserving hashing for appliance identification based on V-I trajectory

Similarity preserving hashing for appliance identification based on V-I trajectory

An Overview of Non-Intrusive Load Monitoring Based on V-I Trajectory Signature

Simulation‐data‐driven load disaggregation based on multi‐channel neural network for industrial and commercial users

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