Spatio-temporal Convolutional Network Based Power Forecasting of Multiple Wind Farms

The rapidly increasing wind power penetration presents new challenges to the operation of power systems. Improving the accuracy of wind power forecasting is a possible solution under this circumstance. In the power forecasting of multiple wind farms, determining the spatio-temporal correlation of multiple wind farms is critical for improving the forecasting accuracy. This paper proposes a spatio-temporal convolutional network (STCN) that utilizes a directed graph convolutional structure. A temporal convolutional network is also adopted to characterize the temporal features of wind power. Historical data from 15 wind farms in Australia are used in the case study. The forecasting results show that the proposed model has higher accuracy than the existing methods. Based on the structure of the STCN, asymmetric spatial correlation at different temporal scales can be observed, which shows the effectiveness of the proposed model.