Wind Power Interval Prediction Based on CGAN and KELM under Extreme Weather Scenarios

As the proportion of wind power in grid continues to increase, more and more newly built or expanded wind farms are facing problems such as lack of historical samples and insufficient training of prediction models. With the increasing frequency of extreme weather events, most wind farms also face problems such as scarce samples of extreme weather scenarios and large power forecasting errors. In response to the abovementioned issues, this paper proposes an wind power interval prediction method based on conditional generative adversarial networks (CGAN) and kernel extreme learning machines (KELM). With the historical samples of extreme weather scenarios, a large number of new samples are generated through CGAN. Based on KELM, an interval prediction model under extreme weather is constructed. Compared to point prediction, this model can provide richer probability information, and compared to other interval prediction methods, the results show an effective improvement in prediction performance.