  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.
