  Recent years have noticed an increasing interest among academia and industry
towards analyzing the electrical consumption of residential buildings and
employing smart home energy management systems (HEMS) to reduce household
energy consumption and costs. HEMS has been developed to simulate the
statistical and functional properties of actual smart grids. Access to publicly
available datasets is a major challenge in this type of research. The potential
of artificial HEMS applications will be further enhanced with the development
of time series that represent different operating conditions of the synthetic
systems. In this paper, we propose a novel variational auto-encoder-generative
adversarial network (VAE-GAN) technique for generating time-series data on
energy consumption in smart homes. We also explore how the generative model
performs when combined with a Q-learning-based HEMS. We tested the online
performance of Q-learning-based HEMS with real-world smart home data. To test
the generated dataset, we measure the Kullback-Leibler (KL) divergence, maximum
mean discrepancy (MMD), and the Wasserstein distance between the probability
distributions of the real and synthetic data. Our experiments show that
VAE-GAN-generated synthetic data closely matches the real data distribution.
Finally, we show that the generated data allows for the training of a
higher-performance Q-learning-based HEMS compared to datasets generated with
baseline approaches.
