A Conditional Generative adversarial Network for energy use in multiple buildings using scarce data

Building consumption data is integral to numerous applications including retrofit analysis, Smart Grid integration and optimization, and load forecasting. Still, due to technical limitations, privacy concerns and the proprietary nature of the industry, usable data is often unavailable for research and development. Generative adversarial networks (GANs) - which generate synthetic instances that resemble those from an original training dataset - have been proposed to help address this issue. Previous studies use GANs to generate building sequence data, but the models are not typically designed for time series problems, they often require relatively large amounts of input data (at least 20,000 sequences) and it is unclear whether they correctly capture the temporal behaviour of the buildings. In this work we implement a conditional temporal GAN that addresses these issues, and we show that it exhibits state-of-the-art performance on small datasets. 22 different experiments that vary according to their data inputs are benchmarked using Jensen-Shannon divergence (JSD) and predictive forecasting validation error. Of these, the best performing is also evaluated using a curated set of metrics that extends those of previous work to include PCA, deep-learning based forecasting and measurements of trend and seasonality. Two case studies are included: one for residential and one for commercial buildings. The model achieves a JSD of 0.012 on the former data and 0.037 on the latter, using only 396 and 156 original load sequences, respectively.