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Generating Energy Data for Machine Learning with Recurrent Generative Adversarial Networks

Author

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  • Mohammad Navid Fekri

    (Department of Electrical and Computer Engneering, The University of Western Ontario, London, ON N6A 5B9, Canada)

  • Ananda Mohon Ghosh

    (Department of Electrical and Computer Engneering, The University of Western Ontario, London, ON N6A 5B9, Canada)

  • Katarina Grolinger

    (Department of Electrical and Computer Engneering, The University of Western Ontario, London, ON N6A 5B9, Canada)

Abstract

The smart grid employs computing and communication technologies to embed intelligence into the power grid and, consequently, make the grid more efficient. Machine learning (ML) has been applied for tasks that are important for smart grid operation including energy consumption and generation forecasting, anomaly detection, and state estimation. These ML solutions commonly require sufficient historical data; however, this data is often not readily available because of reasons such as data collection costs and concerns regarding security and privacy. This paper introduces a recurrent generative adversarial network (R-GAN) for generating realistic energy consumption data by learning from real data. Generativea adversarial networks (GANs) have been mostly used for image tasks (e.g., image generation, super-resolution), but here they are used with time series data. Convolutional neural networks (CNNs) from image GANs are replaced with recurrent neural networks (RNNs) because of RNN’s ability to capture temporal dependencies. To improve training stability and increase quality of generated data, Wasserstein GANs (WGANs) and Metropolis-Hastings GAN (MH-GAN) approaches were applied. The accuracy is further improved by adding features created with ARIMA and Fourier transform. Experiments demonstrate that data generated by R-GAN can be used for training energy forecasting models.

Suggested Citation

  • Mohammad Navid Fekri & Ananda Mohon Ghosh & Katarina Grolinger, 2019. "Generating Energy Data for Machine Learning with Recurrent Generative Adversarial Networks," Energies, MDPI, vol. 13(1), pages 1-23, December.
    • Handle: RePEc:gam:jeners:v:13:y:2019:i:1:p:130-:d:302185
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    References listed on IDEAS

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    1. Lazos, Dimitris & Sproul, Alistair B. & Kay, Merlinde, 2014. "Optimisation of energy management in commercial buildings with weather forecasting inputs: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 39(C), pages 587-603.
    2. Deb, Chirag & Zhang, Fan & Yang, Junjing & Lee, Siew Eang & Shah, Kwok Wei, 2017. "A review on time series forecasting techniques for building energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 902-924.
    3. Zhou, Kaile & Fu, Chao & Yang, Shanlin, 2016. "Big data driven smart energy management: From big data to big insights," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 215-225.
    4. Amasyali, Kadir & El-Gohary, Nora M., 2018. "A review of data-driven building energy consumption prediction studies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1192-1205.
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    Cited by:

    1. Bilgi Yilmaz, 2024. "Housing GANs: Deep Generation of Housing Market Data," Computational Economics, Springer;Society for Computational Economics, vol. 64(1), pages 579-594, July.

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