Day-ahead renewable scenario forecasts based on generative adversarial networks

With the increasing penetration of renewable resources, such as wind and solar, the operation and planning of power systems, especially in large-scale integration, are faced with great risks due to the inherent stochasticity of natural resources. Although this uncertainty is anticipated, their timing, magnitude and duration cannot be predicted accurately. In addition, the renewable power outputs are correlated in space and time and bring further challenges in characterizing their behaviors. To address these issues, this paper provides a data-driven method to forecast renewable scenarios considering its spatiotemporal correlations based on generative adversarial networks (GANs), which has the ability to generated realistic samples from an unknown distribution making them one of the hottest areas in artificial intelligence research. We first utilize GANs to learn the intrinsic patterns and model the dynamic processes of renewable energy sources. Then by solving an optimization problem, we are able to generate large number of day-ahead forecasting scenarios. For validation, we use power generation data from NREL wind and solar integration data sets. The experimental results of this present research accord with the expectations.