Synthetic Seasonal Weekly Load Profile Generation Based on Advanced Wasserstein-Distance Generative Adversarial Networks
TL;DR
The capability of the proposed framework to generate SWLPs preserving the statistical and temporal characteristics of real electricity consumption data is demonstrated, demonstrating a good agreement between real and synthetic data across all identified consumption-pattern clusters.
Abstract
The power injections variability due to volatile renewable energy sources and large dynamic loads (e.g., Plug-in Electric Vehicles and Heat Pumps) may cause excessive voltage fluctuations and power system instability. To mitigate these problems, accurate load profiles are needed to support both grid operation and planning. However, real load profiles are not always readily and fully available due to technical and privacy constraints, limiting their applicability and the possibility of extrapolating consumption patterns for prospective studies. Synthetic load profile generation offers a practical alternative to address these limitations, while preserving data privacy and accessibility. This paper presents a deep learning framework that combines Wasserstein Generative Adversarial Networks with Gradient Penalty (WGAN-GP) to produce artificial, but data-driven seasonal weekly load profiles (SWLP). The training process for each cluster uses separate WGANs, which implement the Wasserstein loss function together with gradient penalty to ensure stable training, while preventing mode collapse. The quality of the synthetic profiles is evaluated using statistical and distribution-based metrics. The proposed WGAN-GP achieved an average Wasserstein distance of 0.042 with a pattern correlation coefficient of about 0.98 with respect to the real load profiles derived from an Irish residential dataset. In comparison, the WGAN without gradient penalty returned am averageWasserstein distance of 0.352, while a Variational Autoencoder (VAE) used as a benchmark achieved an average Wasserstein distance of 0.108 with a pattern correlation coefficient of 0.92. Mean profile comparisons and load distribution analyses showed a good agreement between real and synthetic data across all identified consumption-pattern clusters. These results demonstrate the capability of the proposed framework to generate SWLPs preserving the statistical and temporal characteristics of real electricity consumption data.