Generating Energy Data for Machine Learning with Recurrent Generative Adversarial Networks

Mohammad Navid Fekri, Ananda Mohon Ghosh and Katarina Grolinger
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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

Energies, 2019, vol. 13, issue 1, 1-23

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.

Keywords: energy forecasting; generative adversarial network; recurrent neural network; generative model; Fourier transform; ARIMA; energy data (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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