Wind power forecasting based on principle component phase space reconstruction. (September 2015)

• Record Type: Journal Article
• Authors: Han, Li; Romero, Carlos E.; Yao, Zheng
• Is Part Of: Renewable energy. Volume 81(2015)
• Page Start: 737
• Page End: 744
• Publication Date: 2015-09
• Subjects: Wind power forecasting -- Phase space construction -- Principle component analysis -- Resource allocating networks; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.renene.2015.03.037
• Languages: English
• ISSNs: 0960-1481

Abstract

Abstract: Forecasting of wind power is very important for both power grid and electricity market. Wind power forecasting based only on historical wind power data is carried out in this work. In a first treatment to the wind power data, Phase Space Reconstruction (PSR) is used to reconstruct the phase space of the wind dynamical system. Secondly, Principle Component Analysis (PCA) is used to minimize the influence from improper selection of the delay time and phase dimension. Finally, a prediction model, using Resource Allocating Network (RAN), is built for nonlinear mapping between the historical wind power data and the forecasting. Performance of the proposed method is compared with Persistence (PER), New-Reference (NR), and Adaptive Wavelet Neural Network (AWNN) models by using data from the US National Renewable Energy Laboratory (NREL). Analysis results indicate that the forecasting error of the proposed method is about 3% for 48 look-ahead hours, which is remarkably below the errors obtained with other forecast methods and has a probability close to 80% for 48 look-ahead hours forecasting within 12.5% error. The proposed method can also forecast wind power for turbines of different capacity and at different elevations below 10% error. Highlights: Dynamics of wind power systems analyzed with Phase Space Reconstruction (PSR) and Principal Component Analysis (PCA). Wind power forecasting modeling using Resource Allocating Networks (RAN). Combined approach makes possible to forecast wind power with a long look-ahead horizon, 48 look-ahead hours.