Ultra-short-term Wind Power Prediction Based on Dynamical Ensemble Least Square Support Vector Regression

作者：Liu Rongsheng; Peng Minfang; Zhang Haiyan; Wan Xun; Shen Meie

来源：Journal of Hunan University(Natural Sciences), 2017, 44(4): 79-86.

DOI：10.16339/j.cnki.hdxbzkb.2017.04.011

• Dynamical Ensemble
• Least square support vector regressions
• Numerical weather prediction
• Short-Term wind power predictions
• Time Warp

For the limitation of least square support vector regression (LSSVR) in modeling the time varying feature of wind power, an ultra-short-term wind power prediction (USTWPP) model based on dynamical ensemble LSSVR was proposed. Firstly, the off-line LSSVR model library was created by making use of the historical data which were obtained from Numerical Weather Prediction (NWP) and supervisory control and data acquisition (SCADA) system of wind farm. Then, the candidate members of ensemble LSSVR were selected from off-line LSSVR model library dynamically according to the similarity between the NWP of forecasting period and the NWP of training period. The ensemble members were decided by considering the accuracy and diversity. Finally, the weights of ensemble LSSVR members were assigned according to the similarity between the NWP of training and NWP of prediction period. The validity of the dynamical ensemble LSSVR based predictor was verified by predicting the wind power of a wind farm in Hunan Province. Compared with persistence method (PM), auto regressive integrated moving average (AGIMA), LSSVR, constant weight ensemble LSSVR, and ensemble artificial neural networks (ANN), the dynamical ensemble LSSVR is more accurate, especially when the weather changes severely.

• 出版日期2017
• 单位湖南大学