April 2, 2024, 7:42 p.m. | Chun Fu, Hussain Kazmi, Matias Quintana, Clayton Miller

cs.LG updates on arXiv.org arxiv.org

arXiv:2404.00525v1 Announce Type: new
Abstract: Advances in machine learning and increased computational power have driven progress in energy-related research. However, limited access to private energy data from buildings hinders traditional regression models relying on historical data. While generative models offer a solution, previous studies have primarily focused on short-term generation periods (e.g., daily profiles) and a limited number of meters. Thus, the study proposes a conditional diffusion model for generating high-quality synthetic energy data using relevant metadata. Using a dataset …

abstract advances arxiv building buildings computational cs.lg cs.sy data diffusion eess.sy energy generative generative models historical data however machine machine learning metadata power progress regression research solution studies synthetic type

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