On 552 German households from OpenMeter, a conditional Wasserstein GAN and a Bernstein normalizing flow (MABF) generate the most realistic synthetic 15-minute residential power profiles, outperforming diffusion, hidden Markov, and standard load profile baselines.
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Time-series surrogates from energy consumers generated by machine learning approaches for long-term forecasting scenarios
On 552 German households from OpenMeter, a conditional Wasserstein GAN and a Bernstein normalizing flow (MABF) generate the most realistic synthetic 15-minute residential power profiles, outperforming diffusion, hidden Markov, and standard load profile baselines.