About the event
Scenario generation is an important step in the operation and planning of power systems.
In this talk, we present a data-driven approach for scenario generation using the popular generative adversarial networks, where to deep neural networks are used in tandem. Compared with existing methods that are often hard to scale or sample from, our method is easy to train, robust, and captures both spatial and temporal patterns in renewable generation. In addition, we show that different conditional information can be embedded in the framework. Because of the feed forward nature of the neural networks, scenarios can be generated extremely efficiently.