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Recent years have noticed an increasing interest among academia and industrytowards analyzing the electrical consumption of residential buildings andemploying smart home energy management systems (HEMS) to reduce householdenergy consumption and costs. HEMS has been developed to simulate thestatistical and functional properties of actual smart grids. Access to publiclyavailable datasets is a major challenge in this type of research. The potentialof artificial HEMS applications will be further enhanced with the developmentof time series that represent different operating conditions of the syntheticsystems. In this paper, we propose a novel variational auto-encoder-generativeadversarial network (VAE-GAN) technique for generating time-series data onenergy consumption in smart homes. We also explore how the generative modelperforms when combined with a Q-learning-based HEMS. We tested the onlineperformance of Q-learning-based HEMS with real-world smart home data. To testthe generated dataset, we measure the Kullback-Leibler (KL) divergence, maximummean discrepancy (MMD), and the Wasserstein distance between the probabilitydistributions of the real and synthetic data. Our experiments show thatVAE-GAN-generated synthetic data closely matches the real data distribution.Finally, we show that the generated data allows for the training of ahigher-performance Q-learning-based HEMS compared to datasets generated withbaseline approaches.

Smart Home Energy Management: VAE-GAN synthetic dataset generator and Q-learning