A GAN-Enhanced Ensemble Model for Energy Consumption Forecasting in Large Commercial Buildings

Abstract

Building an accurate and robust energy consumption prediction model is a core mission for the energy management and operation system of a smart building. Prior efforts have explored numerous predictive models in various load prediction scenarios. However, the joint effect of data augmentation and ensemble learning in energy forecasting has not been fully explored. In this paper, we propose a generative adversarial network (GAN)-enhanced ensemble model for energy consumption forecasting in large commercial buildings. The ensemble model aggregates five constituent models with a stacking ensemble method. In addition, we employ a GAN to learn the sample distribution from the original dataset and generate high-quality samples to enhance the training set. The augmented dataset allows a model to be trained with more diverse samples to increase its robustness. A series of experiments are conducted to validate the proposed method with three GAN variants using three performance metrics, including mean absolute error (MAE), root mean square error (RMSE), and coefficient of variation of root mean square error (CVRMSE). Results demonstrate the GAN-enhanced ensemble models are more robust with consistent improvement in reducing the prediction errors. The best ensemble model, enhanced by the Information Maximizing GAN (InfoGAN), outperforms the model without augmentation by decreasing the average error by 1.71, 1.63, and 4.72%, in MAE, RMSE, and CVRMSE respectively, validating its piratical value for building an energy consumption forecasting model in a real-world system.

Keywords

• Energy consumption forecasting
• machine learning
• ensemble model
• generative adversarial network
• data augmentation