Designing a Novel Dataset for Non-intrusive Load Monitoring

Renaux, Douglas P. B.; Linhares, Robson R.; Pöttker, Fabiana; Lazzaretti, André E.; Lima, Carlos R. Erig; Neto, Adil Coelho; Campaner, Mateus

doi:10.1109/sbesc.2018.00045

Cited by 13 publications

(21 citation statements)

References 4 publications

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“…LIT-dataset [ 14 ] has 26 types of appliances such as microwave, fan, television, and so on. It provides signals of current and voltage of each appliance with frequency sampling 15.360.…”

Section: Experiments Results and Discussionmentioning

confidence: 99%

“…After that, we use the trained model to evaluate target load generated by GAN. We implement and evaluate our proposed method using public dataset from Laboratory for Innovation and Technology in Embedded Systems, called LIT-dataset [ 14 ]. The experiment results show that our proposed method is powerful to generate the target load across the complex load by denosing background load.…”

Section: Introductionmentioning

confidence: 99%

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Background Load Denoising across Complex Load Based on Generative Adversarial Network to Enhance Load Identification

Mukaroh

Kim

2020

Sensors

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Non-Intrusive Load Monitoring (NILM) allows load identification of appliances through a single sensor. By using NILM, users can monitor their electricity consumption, which is beneficial for energy efficiency or energy saving. In advance NILM systems, identification of appliances on/off events should be processed instantly. Thus, it is necessary to use an extremely short period signal of appliances to shorten the time delay for users to acquire event information. However, acquiring event information from a short period signal raises another problem. The problem is target load feature to be easily mixed with background load. The more complex the background load has, the noisier the target load occurs. This issue certainly reduces the appliance identification performance. Therefore, we provide a novel methodology that leverages Generative Adversarial Network (GAN) to generate noise distribution of background load then use it to generate a clear target load. We also built a Convolutional Neural Network (CNN) model to identify load based on single load data. Then we use that CNN model to evaluate the target load generated by GAN. The result shows that GAN is powerful to denoise background load across the complex load. It yields a high accuracy of load identification which could reach 92.04%.

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“…LIT-dataset [ 14 ] has 26 types of appliances such as microwave, fan, television, and so on. It provides signals of current and voltage of each appliance with frequency sampling 15.360.…”

Section: Experiments Results and Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Background Load Denoising across Complex Load Based on Generative Adversarial Network to Enhance Load Identification

Mukaroh

Kim

2020

Sensors

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“…The selected benchmark datasets were as follows: (i) reference energy disaggregation dataset (REDD) [ 23 ], (ii) United Kingdom domestic appliance-level electricity dataset (UK-DALE) [ 24 ], (iii) worldwide household and industry transient energy dataset (WHITED) [ 25 ], (iv) controlled on/off loads library dataset (COOLL) [ 26 ], and (v) laboratory for innovation and technology in embedded systems dataset (LIT) [ 27 ]. Table 1 summarizes the characteristics of the datasets, including country, number of classes, data duration, and sampling rate.…”

Section: Datasets and Methodologymentioning

confidence: 99%

Handling Data Heterogeneity in Electricity Load Disaggregation via Optimized Complete Ensemble Empirical Mode Decomposition and Wavelet Packet Transform

Chui

Gupta

Liu

et al. 2021

Sensors

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Global warming is a leading world issue driving the common social objective of reducing carbon emissions. People have witnessed the melting of ice and abrupt changes in climate. Reducing electricity usage is one possible method of slowing these changes. In recent decades, there have been massive worldwide rollouts of smart meters that automatically capture the total electricity usage of houses and buildings. Electricity load disaggregation (ELD) helps to break down total electricity usage into that of individual appliances. Studies have implemented ELD models based on various artificial intelligence techniques using a single ELD dataset. In this paper, a powerline noise transformation approach based on optimized complete ensemble empirical model decomposition and wavelet packet transform (OCEEMD–WPT) is proposed to merge the ELD datasets. The practical implications are that the method increases the size of training datasets and provides mutual benefits when utilizing datasets collected from other sources (especially from different countries). To reveal the effectiveness of the proposed method, it was compared with CEEMD–WPT (fixed controlled coefficients), standalone CEEMD, standalone WPT, and other existing works. The results show that the proposed approach improves the signal-to-noise ratio (SNR) significantly.

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“…Changes in power or current are associated with signal analysis in time-domain, in which the event detection is based, for instance, on the apparent power to determine the instant of an event, as presented in the Half-Cycle Apparent Power (HCApP) method, proposed in [9]. A similar approach, using the envelope of electrical current and an adaptive threshold-based method, is presented in [10,11], defined as High-Accuracy NILM Detector (HAND).…”

Section: Review Of Techniques For Load Event Detection and Power Signature Recognitionmentioning

confidence: 99%

“…Generally, these methods are less susceptible to noise and present load recognition accuracies above 90%, even when classifying multiple loads, as discussed in [21]. In [9,16], the HCApP can also be used to extract features based on active, reactive, and apparent power of a signal to perform classification, even though some features can be extracted during transients. Still in this context, a Long-Short-Term Memory Recurrent Neural Network (LSTM-RNN) model is proposed in [22], to directly disaggregate load power signals.…”

Section: Review Of Techniques For Load Event Detection and Power Signature Recognitionmentioning

confidence: 99%

A Multi-Agent NILM Architecture for Event Detection and Load Classification

et al. 2020

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A multi-agent architecture for a Non-Intrusive Load Monitoring (NILM) solution is presented and evaluated. The underlying rationale for such an architecture is that each agent (load event detection, feature extraction, and classification) outperforms others of the same type in particular scenarios; hence, by combining the expertise of these agents, the system presents an improved performance. Known NILM algorithms, as well as new algorithms, proposed by the authors, were individually evaluated and compared. The proposed architecture considers a NILM system composed of Load Monitoring Modules (LMM) that report to a Center of Operations, required in larger facilities. For the purposed of evaluating and comparing performance, five load event detect agents, five feature extraction agents, and five classification agents were studied so that the best combinations of agents could be implemented in LMMs. To evaluate the proposed system, the COOLL and the LIT-Dataset were used. Performance improvements were detected in all scenarios, with power-ON and power-OFF detection improving up to 13%, while classification accuracy improved up to 9.4%.

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Designing a Novel Dataset for Non-intrusive Load Monitoring

Cited by 13 publications

References 4 publications

Background Load Denoising across Complex Load Based on Generative Adversarial Network to Enhance Load Identification

Background Load Denoising across Complex Load Based on Generative Adversarial Network to Enhance Load Identification

Handling Data Heterogeneity in Electricity Load Disaggregation via Optimized Complete Ensemble Empirical Mode Decomposition and Wavelet Packet Transform

A Multi-Agent NILM Architecture for Event Detection and Load Classification

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