Variational Autoencoder Generative Adversarial Network for Synthetic Data Generation in Smart Home Article Swipe
Mina Razghandi
,
Hao Zhou
,
Melike Erol‐Kantarci
,
Damla Turgut
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2201.07387
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2201.07387
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.
Related Topics
Concepts
Autoencoder
Computer science
Generative model
Divergence (linguistics)
Key (lock)
Smart grid
Synthetic data
Data mining
Data modeling
Artificial intelligence
Generative adversarial network
Data quality
Generative grammar
Grid
Machine learning
Deep learning
Mathematics
Engineering
Geometry
Linguistics
Operations management
Philosophy
Computer security
Electrical engineering
Database
Metric (unit)
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2201.07387
- https://arxiv.org/pdf/2201.07387
- OA Status
- green
- Cited By
- 7
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4221167279
All OpenAlex metadata
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https://openalex.org/W4221167279Canonical identifier for this work in OpenAlex
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https://doi.org/10.48550/arxiv.2201.07387Digital Object Identifier
- Title
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Variational Autoencoder Generative Adversarial Network for Synthetic Data Generation in Smart HomeWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-01-19Full publication date if available
- Authors
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Mina Razghandi, Hao Zhou, Melike Erol‐Kantarci, Damla TurgutList of authors in order
- Landing page
-
https://arxiv.org/abs/2201.07387Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2201.07387Direct link to full text PDF
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2201.07387Direct OA link when available
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Autoencoder, Computer science, Generative model, Divergence (linguistics), Key (lock), Smart grid, Synthetic data, Data mining, Data modeling, Artificial intelligence, Generative adversarial network, Data quality, Generative grammar, Grid, Machine learning, Deep learning, Mathematics, Engineering, Geometry, Linguistics, Operations management, Philosophy, Computer security, Electrical engineering, Database, Metric (unit)Top concepts (fields/topics) attached by OpenAlex
- Cited by
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7Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1, 2023: 6Per-year citation counts (last 5 years)
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.so | 38 |
| abstract_inverted_index.to | 16, 30, 127, 141, 185 |
| abstract_inverted_index.we | 46, 135 |
| abstract_inverted_index.GAN | 120, 173 |
| abstract_inverted_index.The | 175 |
| abstract_inverted_index.and | 7, 37, 72, 101, 110, 122, 148, 158 |
| abstract_inverted_index.any | 82 |
| abstract_inverted_index.can | 25 |
| abstract_inverted_index.due | 29 |
| abstract_inverted_index.for | 11 |
| abstract_inverted_index.key | 138 |
| abstract_inverted_index.on. | 39 |
| abstract_inverted_index.our | 132 |
| abstract_inverted_index.the | 2, 21, 77, 86, 89, 93, 105, 116, 123, 129, 143, 164, 171, 182 |
| abstract_inverted_index.(KL) | 95 |
| abstract_inverted_index.Data | 0 |
| abstract_inverted_index.both | 156 |
| abstract_inverted_index.data | 5, 24, 33, 35, 59, 70, 87, 107, 125, 146, 153, 167, 180 |
| abstract_inverted_index.end, | 42 |
| abstract_inverted_index.five | 137 |
| abstract_inverted_index.from | 76 |
| abstract_inverted_index.fuel | 3 |
| abstract_inverted_index.grid | 13, 58, 145 |
| abstract_inverted_index.load | 109 |
| abstract_inverted_index.many | 17 |
| abstract_inverted_index.mean | 98 |
| abstract_inverted_index.most | 183 |
| abstract_inverted_index.real | 124, 159, 188 |
| abstract_inverted_index.same | 78 |
| abstract_inverted_index.that | 163, 186 |
| abstract_inverted_index.them | 150 |
| abstract_inverted_index.this | 41, 44 |
| abstract_inverted_index.used | 136 |
| abstract_inverted_index.data. | 160, 189 |
| abstract_inverted_index.issue | 28 |
| abstract_inverted_index.model | 61, 169 |
| abstract_inverted_index.other | 18 |
| abstract_inverted_index.prior | 83 |
| abstract_inverted_index.size, | 34 |
| abstract_inverted_index.smart | 12, 57, 144 |
| abstract_inverted_index.types | 68 |
| abstract_inverted_index.which | 62 |
| abstract_inverted_index.(MMD), | 100 |
| abstract_inverted_index.before | 88 |
| abstract_inverted_index.model, | 118 |
| abstract_inverted_index.model. | 133 |
| abstract_inverted_index.models | 157 |
| abstract_inverted_index.paper, | 45 |
| abstract_inverted_index.Network | 53 |
| abstract_inverted_index.VAE-GAN | 178 |
| abstract_inverted_index.between | 104, 151 |
| abstract_inverted_index.capable | 64 |
| abstract_inverted_index.fields. | 19 |
| abstract_inverted_index.machine | 8 |
| abstract_inverted_index.maximum | 97 |
| abstract_inverted_index.privacy | 31 |
| abstract_inverted_index.propose | 47 |
| abstract_inverted_index.samples | 75 |
| abstract_inverted_index.science | 6 |
| abstract_inverted_index.similar | 15 |
| abstract_inverted_index.vanilla | 119, 172 |
| abstract_inverted_index.various | 67 |
| abstract_inverted_index.without | 80 |
| abstract_inverted_index.However, | 20 |
| abstract_inverted_index.analysis | 84 |
| abstract_inverted_index.compared | 92, 149 |
| abstract_inverted_index.describe | 142 |
| abstract_inverted_index.distance | 103 |
| abstract_inverted_index.evaluate | 128 |
| abstract_inverted_index.indicate | 162 |
| abstract_inverted_index.learning | 9, 66 |
| abstract_inverted_index.network, | 121 |
| abstract_inverted_index.network. | 174 |
| abstract_inverted_index.phase.We | 91 |
| abstract_inverted_index.proposed | 117, 165 |
| abstract_inverted_index.quality, | 36 |
| abstract_inverted_index.training | 90 |
| abstract_inverted_index.(VAE-GAN) | 54 |
| abstract_inverted_index.concerns, | 32 |
| abstract_inverted_index.generated | 114, 154 |
| abstract_inverted_index.plausible | 74 |
| abstract_inverted_index.synthetic | 106, 152, 166, 179 |
| abstract_inverted_index.Generative | 51 |
| abstract_inverted_index.comparable | 184 |
| abstract_inverted_index.generating | 73 |
| abstract_inverted_index.generative | 60, 168 |
| abstract_inverted_index.parameters | 140 |
| abstract_inverted_index.performing | 81 |
| abstract_inverted_index.techniques | 10 |
| abstract_inverted_index.(electrical | 108 |
| abstract_inverted_index.Adversarial | 52 |
| abstract_inverted_index.AutoEncoder | 50 |
| abstract_inverted_index.Experiments | 161 |
| abstract_inverted_index.Variational | 49 |
| abstract_inverted_index.Wasserstein | 102 |
| abstract_inverted_index.discrepancy | 99 |
| abstract_inverted_index.divergence, | 96 |
| abstract_inverted_index.outperforms | 170 |
| abstract_inverted_index.performance | 130 |
| abstract_inverted_index.production) | 112 |
| abstract_inverted_index.statistical | 139 |
| abstract_inverted_index.Furthermore, | 134 |
| abstract_inverted_index.availability | 22 |
| abstract_inverted_index.distribution | 79, 113, 147, 176 |
| abstract_inverted_index.applications, | 14 |
| abstract_inverted_index.distribution, | 126 |
| abstract_inverted_index.distributions | 71 |
| abstract_inverted_index.Kullback-Leibler | 94 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/9 |
| sustainable_development_goals[0].score | 0.46000000834465027 |
| sustainable_development_goals[0].display_name | Industry, innovation and infrastructure |
| citation_normalized_percentile |