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Abstract <p>This article addresses the problem of synthetic tabular data generation, specifically energy consumption of high performance computing resources. The model proposed by Wasserstein generative adversarial Networks is applied to create realistic synthetic data with statistical properties similar to real data, which are useful for energy efficiency studies when data are scarce and obtaining new data implies costly experiments. Different energy models were developed and applied to data obtained from experiments on real computing infrastructures, involving four high-end computing nodes. The similarity analysis of the obtained distribution showed that the generated synthetic data have similar statistical properties to real data. The quality of estimation models was satisfactory when using generated data instead of real data with generated data and improved when using both. Also, the obtained synthetic data outperforms SMONG, a generation method for regression, in terms of data independence and diversity, resulting in a more robust and less biased dataset.</p>

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Synthetic Energy Data Generation Using Wasserstein GANs for High Performance Computing Nodes

  • 期刊论文

  • 发表日期：

    2026年6月23日

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  • Jonathan Muraña,
  • Sergio Nesmachnow

摘要

Abstract

This article addresses the problem of synthetic tabular data generation, specifically energy consumption of high performance computing resources. The model proposed by Wasserstein generative adversarial Networks is applied to create realistic synthetic data with statistical properties similar to real data, which are useful for energy efficiency studies when data are scarce and obtaining new data implies costly experiments. Different energy models were developed and applied to data obtained from experiments on real computing infrastructures, involving four high-end computing nodes. The similarity analysis of the obtained distribution showed that the generated synthetic data have similar statistical properties to real data. The quality of estimation models was satisfactory when using generated data instead of real data with generated data and improved when using both. Also, the obtained synthetic data outperforms SMONG, a generation method for regression, in terms of data independence and diversity, resulting in a more robust and less biased dataset.