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

Abstract

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%.

Scattering Transform for Classification in Non-Intrusive Load Monitoring

“…This proposed structure is quite complex and requires two learning processes: one for the GAN and another for the CNN applied to classification. We reach an accuracy 8.48% greater than [37] with a more straightforward approach.…”

“…The accuracy of the proposed method with the PLAID dataset was 2.10% greater than [33]. With the LIT-SYN dataset, our approach showed accuracy approximately equivalent to [7] and 8.48% greater than [37].…”

“…The proposal of [37] dealt with the load classification task as a denoising problem, and the authors proposed a Generative Adversarial Network to generate the noise distribution of background to clear the target load. This proposed structure is quite complex and requires two learning processes: one for the GAN and another for the CNN applied to classification.…”

“…Although this result was superior to the related literature, applying the multiagent strategy in realistic cases may be compromised due to the high computational complexity. The authors in [37] also presented a CNN-based classification model for NILM, reaching 92% global accuracy on LIT-dataset, but with limitations in the feature extraction for loads with similar transitory shapes.…”

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

“…It is worth noting that extra data generation from the source dataset [ 20 , 21 ] and data simulation [ 22 ] are not related to the topic of this research.…”