Background Load Denoising across Complex Load Based on Generative Adversarial Network to Enhance Load Identification
| Autor: | Thi-Thu-Huong Le, Afifatul Mukaroh, Howon Kim |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Computer science
Noise reduction Real-time computing 02 engineering and technology lcsh:Chemical technology Biochemistry Signal Article Analytical Chemistry 0202 electrical engineering electronic engineering information engineering denoising lcsh:TP1-1185 Electrical and Electronic Engineering Instrumentation Noise (signal processing) Event (computing) business.industry 020208 electrical & electronic engineering Atomic and Molecular Physics and Optics GAN Identification (information) NILM 020201 artificial intelligence & image processing Electricity complex background load identification business Energy (signal processing) CNN Efficient energy use |
| Zdroj: | Sensors (Basel, Switzerland) Sensors Volume 20 Issue 19 Sensors, Vol 20, Iss 5674, p 5674 (2020) |
| ISSN: | 1424-8220 |
| Popis: | 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%. |
| Databáze: | OpenAIRE |
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