Exploring of deep learning synthetic data generation for sensitive utility data sharing / by Benjamin Santos, Rafik Chemli, and Julian Templeton. : CS11-522/2024-1-17E-PDF

"Utilities hold crucial information about energy usage and building characteristics which can be utilized by government agencies to improve their corresponding analytics. However, this data is associated with private customer records and thus the building data and energy usage may be too sensitive to share. Often, high-level aggregated versions of this data are shared through robust contracts, limiting the statistics that can be derived. With the advancement of generative machine learning techniques, Statistics Canada and Natural Resources Canada have explored the feasibility of using these models to produce synthetic versions of utility data which may be shared in full to requesting organizations"--Abstract, page [2].

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