A Very Short Term Wind Power Forecasting Approach based on Numerical Weather Prediction and Error Correction Method

摘要：

　　The rapid development of wind power brings great stability challenge to power system due to the randomicity and uncertainty of wind power output. One effective way to overcome the challenge is wind power forecasting. A novel very short-term wind power forecasting approach based on numerical weather prediction and error correction method is presented in the paper. From the statistical analysis of the error probability density distribution of Back Propagation Neural Network (BPNN) and Support Vector Machine (SVM) based wind power prediction, an error estimation model is presented and an error correction method is proposed. The key step of correction method is calculating the corresponding prediction uncertainties according to the predict value. This method combines the advantages of both error estimation model and error probability density distribution, which is evaluated with the data from 7 wind farms and numerical weather prediction, and is proved to be an effective way to improve the accuracy of very short term wind power forecasting.

关键词： Wind Power Forecasting Error Correction Support Vector Machine Statistical Analysis

作者: Xiaosheng Peng Diyuan Deng Jinyu Wen Lei Xiong Shuanglei Feng Bo Wang

作者单位: School of Electrical and Electronic Engineering,Huazhong University of Science and Technology,Wuhan, China Electric Power Research Institute,Haidian District,Beijing,China

会议类型: 国际会议

会议名称: 2016中国国际供电会议(CICED2016)

会议地点: 西安

会议语种:英文

页码: 1-4

在线出版日期: 2016-09-01（万方平台首次上网日期，不代表论文的发表时间）