A Modified IP-Based NILM Approach Using Appliance Characteristics Extracted by 2-SAX

Liu, Yaoxian; Sun, Yi; Li, Bin

doi:10.1109/access.2019.2910310

Cited by 8 publications

(5 citation statements)

References 30 publications

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“…As transferability approaches are a relatively recent direction within the area of NILM, only few approaches have been discussed in the literature [23]- [27]. Specifically, most of the proposed approaches investigate previously published architectures in terms of their transferability capability and evaluate their performance on cross domain learning [23].…”

Section: Transferability For Nilmmentioning

confidence: 99%

“…Therefore, the more recent evaluations have considered the transferability capability of the NILM architecture. However, only few papers provide distinct discussions on the specific case of transfer learning in the context of NILM [23]- [27] based on the usage of real data. In detail, in [27] an approach based on voltage/currenttrajectories with dedicated feature colouring and usage of image-processing deep learning models is presented.…”

Section: Introductionmentioning

confidence: 99%

“…However, only few papers provide distinct discussions on the specific case of transfer learning in the context of NILM [23]- [27] based on the usage of real data. In detail, in [27] an approach based on voltage/currenttrajectories with dedicated feature colouring and usage of image-processing deep learning models is presented. However, the approach is only evaluated on appliances signals and not on the aggregated signal.…”

Section: Introductionmentioning

confidence: 99%

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Device and Time Invariant Features for Transferable Non-Intrusive Load Monitoring

Schirmer

Mporas

2022

IEEE Open J. Power Energy

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Non-Intrusive Load Monitoring aims to extract the energy consumption of individual electrical appliances through disaggregation of the total power consumption as measured by a single smart meter in a household. Although when data from the same household are used to train a disaggregation model the device disaggregation accuracy is quite high (80% -95%), depending on the number of devices, the use of pre-trained disaggregation models in new households in most cases results in a significant reduction of disaggregation accuracy. In this article we propose a transferability approach for Non-Intrusive Load Monitoring using fractional calculus and normalized Karhunen Loeve Expansion based spectrograms followed by a Convolutional Neural Network in order to generate device characteristic features that do not change significantly across different households. The performance of the proposed methodology was evaluated using two publicly available datasets, namely REDD and REFIT. The proposed transferability approach improves the Mean Absolute Error by 13.1% when compared to other transfer learning approaches for energy disaggregation.

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Section: Transferability For Nilmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Device and Time Invariant Features for Transferable Non-Intrusive Load Monitoring

Schirmer

Mporas

2022

IEEE Open J. Power Energy

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“…The ED performance of optimization-based algorithms largely depends on the objective functions and related constraints. Apart from the most commonly employed objective function -least square error (LSE) between measured and approximated aggregate power consumption [19], there are several enhancements via median filtering [22], convex penalty term [23] and linear-programming based refinement [24]. However, such optimization based algorithms fail in practical settings as they assume the aggregate energy consumption equals the sum of considered appliances' usage.…”

Section: Related Workmentioning

confidence: 99%

More Behind Your Electricity Bill: a Dual-DNN Approach to Non-Intrusive Load Monitoring

Tang¹,

Huang²,

Wang³

et al. 2021

Preprint

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Non-intrusive load monitoring (NILM) is a wellknown single-channel blind source separation problem that aims to decompose the household energy consumption into itemised energy usage of individual appliances. In this way, considerable energy savings could be achieved by enhancing household's awareness of energy usage. Recent investigations have shown that deep neural networks (DNNs) based approaches are promising for the NILM task. Nevertheless, they normally ignore the inherent properties of appliance operations in the network design, potentially leading to implausible results. We are thus motivated to develop the dual Deep Neural Networks (dual-DNN), which aims to i) take advantage of DNNs' learning capability of latent features and ii) empower the DNN architecture with identification ability of universal properties. Specifically in the design of dual-DNN, we adopt one subnetwork to measure power ratings of different appliances' operation states, and the other subnetwork to identify the running states of target appliances. The final result is then obtained by multiplying these two network outputs and meanwhile considering the multi-state property of household appliances. To enforce the sparsity property in appliance's state operating, we employ median filtering and hard gating mechanisms to the subnetwork for state identification. Compared with the state-of-the-art NILM methods, our dual-DNN approach demonstrates a 21.67% performance improvement in average on two public benchmark datasets.

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“…There are only a few studies on the communication and information processing in the NILM scenario. In order to solve the communication congestion between smart meters and cloud computing centers, reference [19] propose an improved NILM method based on IP and device characteristics. In reference [20], an NILM system is designed for real-time remote monitoring of load operation status and identification of load types.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Cryptography Scheme for NILM Data Security

Feng

Wang

et al. 2020

Electronics

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Using fine-grained data analysis, non-invasive load monitoring (NILM) can reveal the detail of electricity customers’ habits, which is helpful in the improvement of refined management and better user experience. However, the possibility of electricity customers’ privacy leak is also gradually increasing, and the security of NILM data has become a priority problem to be solved. To protect the privacy disclosure of NILM data, this paper analyzes the NILM privacy leak problems and ways in which information leak occurs faced by NILM data. On the basis of the comprehensive survey of cryptographic algorithms to choose the most appropriate data security method for NILM, a hybrid cryptography scheme was proposed to protect the data security. In the scheme, symmetric algorithm AES (Advanced Encryption Standard) was used to encrypt data for high efficiency, and asymmetric algorithm RSA (Rivest-Shamir-Adleman) was used to encrypt AES key for identity authentication. The classical algorithm HMAC-SHA1 (Hash Message Authentication Codes-Secure Hash Algorithm 1) was further developed to guarantee the integrity of data. By transplanting the algorithm into STM32 MCU (STMicroelectronics 32 bit Micro Controller Unit) for performance test and using Visual studio 2017 + QT tools to develop the test interface, one optimal operation mode was selected for the scheme. At the same time, the effectiveness of the scheme was verified, and the scheme computing cost depended on the efficiency of encryption and decryption, or signature and verification of the RSA algorithm.

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A Modified IP-Based NILM Approach Using Appliance Characteristics Extracted by 2-SAX

Cited by 8 publications

References 30 publications

Device and Time Invariant Features for Transferable Non-Intrusive Load Monitoring

Device and Time Invariant Features for Transferable Non-Intrusive Load Monitoring

More Behind Your Electricity Bill: a Dual-DNN Approach to Non-Intrusive Load Monitoring

A Hybrid Cryptography Scheme for NILM Data Security

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