Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Variational Autoencoder Generative Adversarial Network for Synthetic
  Data Generation in Smart Home   Data is the fuel of data science and machine learning techniques for smart
grid applications, similar to many other fields. However, the availability of
data can be an issue due to privacy concerns, data size, data quality, and so
on. To this end, in this paper, we propose a Variational AutoEncoder Generative
Adversarial Network (VAE-GAN) as a smart grid data generative model which is
capable of learning various types of data distributions and generating
plausible samples from the same distribution without performing any prior
analysis on the data before the training phase.We compared the Kullback-Leibler
(KL) divergence, maximum mean discrepancy (MMD), and Wasserstein distance
between the synthetic data (electrical load and PV production) distribution
generated by the proposed model, vanilla GAN network, and the real data
distribution, to evaluate the performance of our model. Furthermore, we used
five key statistical parameters to describe the smart grid data distribution
and compared them between synthetic data generated by both models and real
data. Experiments indicate that the proposed synthetic data generative model
outperforms the vanilla GAN network. The distribution of VAE-GAN synthetic data
is the most comparable to that of real data.

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The categories of this paper are: atom-ph
4
The categories of this paper are: cond-mat
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The categories of this paper are: nucl-ex
7
The categories of this paper are: hep-th
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The categories of this paper are: stat
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The categories of this paper are: econ
26
The categories of this paper are: bayes-an
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The categories of this paper are: hep-lat
25
The categories of this paper are: patt-sol
10
The categories of this paper are: q-bio
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The categories of this paper are: math-ph
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The categories of this paper are: ao-sci
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The categories of this paper are: q-fin
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The categories of this paper are: supr-con
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The categories of this paper are: dg-ga
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The categories of this paper are: gr-qc