The selection method of concrete scenarios based on scenario database and generative models is proposed in this paper. Two sampling methods are used in selecting appropriate parameters along a performance measure. First, the parameter space is extracted from scenario DB, and a set of parameters are selected via random sampling. Once the set of concrete scenarios are simulated, their distribution is analyzed with respect to a specific measure. The second method is based on parameter generative models. Simulated scenarios are used to train a surrogate model, which is a multi-layer perceptron model. Then, a generative model is designed to search for the desired parameter based on the surrogate model. Thus, the second one can be used to compensate for the imbalance in randomly sampled concrete scenarios. Finally, the proposed selection method is more efficient than random sampling from the viewpoint of the distribution of two different measures.
