Short-Term Wind Speed Forecasting Based on Information of Neighboring Wind Farms
Zhongju Wang, Jing Zhang, Zhang Yu, Chao Huang, Long Wang
Abstract
To address the uncertainty caused by integrating wind power into the electricity grid, accurate wind speed forecasting is highly desired. However, historical wind speed data of new wind farms may be insufficient for training a well-performed forecasting model. To address this issue, short-term wind speed forecasting with convolutional neural network (CNN) based on information of neighboring wind farms is studied in this paper. In the proposed approach, the CNN is employed to migrate the intrinsic features of wind speed changes to newly built wind farms. To evaluate the performance of the proposed approach, wind speed data collected from three wind farms in China is utilized and multi-step-ahead forecasting is considered. The computational results prove the proposed approach outperforms benchmarking methods Support Vector Regression, Kernel Ridge Regression, and CNN by only considering data of the target wind farm.