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Generating Energy Data for Machine Learning with Recurrent Generative Adversarial Networks - 2019

Generating Energy Data For Machine Learning With Recurrent Generative Adversarial Networks

Research Area:  Machine Learning


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.


Author(s) Name:  Mohammad Navid Fekri,Ananda Mohon Ghosh and Katarina Grolinger

Journal name:   Energies

Conferrence name:  

Publisher name:  MDPI

DOI:  10.3390/en13010130

Volume Information:  Volume 13 Issue 1