IMPEC: An Integrated System for Monitoring and Processing Electricity Consumption in Buildings

Ahajjam, Mohamed Aymane; Licea, Daniel Bonilla; Ghogho, Mounir; Kobbane, Abdellatif

doi:10.3390/s20041048

Cited by 8 publications

(1 citation statement)

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“… The approach [ 7 ] combines both load identification and energy disaggregation based on unsupervised clustering technique. Recent research [ 8 ] proposes both hardware and software for load identification and power consumption monitoring of individual appliances. However, the design system is quite complex.…”

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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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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Section: Introductionmentioning

confidence: 99%