(Peer-Reviewed) Spatio-Temporal Convolutional Network Based Power Forecasting of Multiple Wind Farms
Xiaochong Dong 董骁翀 ¹, Yingyun Sun 孙英云 ¹, Ye Li 李烨 ², Xinying Wang 王新迎 ², Tianjiao Pu 蒲天骄 ²
¹ School of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, China
中国 北京 华北电力大学电气与电子工程学院
² China Electric Power Research Institute, Beijing 100192, China
中国 北京 中国电力科学研究院
Abstract
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 in 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 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.
Separation and identification of mixed signal for distributed acoustic sensor using deep learning
Huaxin Gu, Jingming Zhang, Xingwei Chen, Feihong Yu, Deyu Xu, Shuaiqi Liu, Weihao Lin, Xiaobing Shi, Zixing Huang, Xiongji Yang, Qingchang Hu, Liyang Shao
Opto-Electronic Advances
2025-11-25
