Synthetic Time-Series Data Generation for Smart Grids Using 3D Autoencoder GAN
Given the growing significance of data-driven approaches in analysis and decision-making in smart grid, the availability of diverse and representative datasets is paramount. However, challenges such as privacy concerns, data size limitations, and data quality issues have constrained the usage of rea...
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| Veröffentlicht in: | IEEE transactions on industrial informatics Jg. 21; H. 7; S. 5047 - 5058 |
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| Abstract | Given the growing significance of data-driven approaches in analysis and decision-making in smart grid, the availability of diverse and representative datasets is paramount. However, challenges such as privacy concerns, data size limitations, and data quality issues have constrained the usage of real-world data. In this paper, we introduce the 3D Autoencoder Generative Adversarial Network (3DAE GAN) as a solution to generate high-resolution and multivariate synthetic time-series data capable of representing various real power consumption patterns across different households and driving data for Electric Vehicles (EVs). Beyond the conventional GAN structure, the incorporation of both the Autoencoder and 3D-convolution processes enables a more comprehensive extraction of patterns in data, thereby addressing limitations present in existing data generation methods. Evaluation results using the Pecan Street and Emobpy simulated EV dataset demonstrate that the proposed method generates synthetic data with higher similarity scores compared to existing approaches. Furthermore, downstream prediction tasks are conducted to establish the comparability between using the original data and the synthetic data, revealing no significant differences. Moreover, the risk of possible information leakage from synthetic data about original data is evaluated by performing membership inference attacks and population attacks on the prediction models that are trained with synthetic data. The robustness of the synthetic data are examined when facing FGSM attacks. |
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| AbstractList | Given the growing significance of data-driven approaches in analysis and decision-making in smart grid, the availability of diverse and representative datasets is paramount. However, challenges such as privacy concerns, data size limitations, and data quality issues have constrained the usage of real-world data. In this paper, we introduce the 3D Autoencoder Generative Adversarial Network (3DAE GAN) as a solution to generate high-resolution and multivariate synthetic time-series data capable of representing various real power consumption patterns across different households and driving data for Electric Vehicles (EVs). Beyond the conventional GAN structure, the incorporation of both the Autoencoder and 3D-convolution processes enables a more comprehensive extraction of patterns in data, thereby addressing limitations present in existing data generation methods. Evaluation results using the Pecan Street and Emobpy simulated EV dataset demonstrate that the proposed method generates synthetic data with higher similarity scores compared to existing approaches. Furthermore, downstream prediction tasks are conducted to establish the comparability between using the original data and the synthetic data, revealing no significant differences. Moreover, the risk of possible information leakage from synthetic data about original data is evaluated by performing membership inference attacks and population attacks on the prediction models that are trained with synthetic data. The robustness of the synthetic data are examined when facing FGSM attacks. |
| Author | Sikdar, Biplab Zhang, Guihai |
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| References | ref13 Wang (ref19) 2015 ref12 ref11 ref10 ref2 ref1 ref17 ref16 Goodfellow (ref25) 2014 Gulrajani (ref15) 2017; 30 ref23 ref20 ref22 Kumar (ref24) 2020 ref21 ref8 Arjovsky (ref14) 2017 ref7 ref9 ref4 INC (ref18) 2023 ref3 ref6 ref5 |
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| SubjectTerms | 3D convolution Autoencoders Correlation Data models Data privacy Datasets Deep learning Electric vehicles Generative adversarial networks generative adversarial networks (GANs) Households Load modeling Multivariate analysis Power demand power grid data Prediction models Smart grid Synthetic data Three-dimensional displays Time series time series generation Training |
| Title | Synthetic Time-Series Data Generation for Smart Grids Using 3D Autoencoder GAN |
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