WindGMMN: Scenario Forecasting for Wind Power Using Generative Moment Matching Networks

With the increasing penetration of wind power generation, the fluctuating and intermittent behavior of wind power poses huge challenges to the operation and planning of distribution networks. A popular way to mitigate these challenges is to provide a group of possible wind power forecasting scenarios instead of depending on deterministic point forecasting values, so that system operators can consider the uncertainties. This letter proposes a novel WindGMMN method for wind power scenario forecasting, in which necessary modifications are made on the generative moment matching network (GMMN), and an optimization strategy is designed to find a series of wind power scenarios with similar shapes, probability distributions, and temporal correlations as potential scenarios. Simulations and analyses were performed on a public dataset with 2190 wind power generation curves and their corresponding meteorological features. The results show that the proposed WindGMMN outperforms popular baselines (e.g., variational auto-encoders and generative adversarial networks) for scenario forecasting of wind power, without any restrictions on the time horizon (e.g., times ranging from 10 min to 24 h).