This research was supported in part by National Natural Science Foundation of China (grant number: No. 62003218) and in part by the Stable Support Projects for Shenzhen Higher Education Institutions (grant number: No. 20220717223051001).
机构署名：
本校为其他机构
院系归属：
电气与信息工程学院
摘要：
The integration of large-scale wind power into the power grid threatens the stable operation of the power system. Traditional wind power prediction is based on time series without considering the variability between wind turbines in different locations. This paper proposes a wind power probability density prediction method based on a time-variant deep feed-forward neural network (ForecastNet) considering a spatio-temporal distribution. First, the outliers in the wind turbine data are detected based on the isolated forest algorithm and repaired through Lagrange interpolation. Then, based on the...
