Creating synthetic energy meter data using conditional diffusion and
  building metadata
Chun Fu Hussain Kazmi Matias Quintana Clayton Miller
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 comprising 1,828 power meters from various buildings and countries,
this model is compared with traditional methods like Conditional Generative
Adversarial Networks (CGAN) and Conditional Variational Auto-Encoders (CVAE).
It explicitly handles long-term annual consumption profiles, harnessing
metadata such as location, weather, building, and meter type to produce
coherent synthetic data that closely resembles real-world energy consumption
patterns. The results demonstrate the proposed diffusion model's superior
performance, with a 36% reduction in Frechet Inception Distance (FID) score and
a 13% decrease in Kullback-Leibler divergence (KL divergence) compared to the
following best method. The proposed method successfully generates high-quality
energy data through metadata, and its code will be open-sourced, establishing a
foundation for a broader array of energy data generation models in the future.