Short term wind power forecasting using Chebyshev polynomial trained by ridge extreme learning machine
Sthita Prajna Mishra, P.K. Dash · 2015
Wind power generation has experienced a rapid growth around the world in the past decade. This highlights the importance of the short-term wind power forecasting. This paper focuses on the short-term wind power forecasting using Single layer Chebyshev polynomial (SLCNN) with regression theory of extreme learning machine (RELM). Input parameters are fed to Functional Expansion Block (FEB). The output matrixes are operated in hidden layer by trigonometric hyperbolic operation with randomized weight and finally output is calculated. To know the performance and accuracy of the proposed model; mean absolute percentage error, mean absolute error and root mean square error are evaluated. The simulations are verified in MATLAB platform. Simulation results and graphs for actual data validate the effectiveness of proposed model. The data is obtained in the real operation of a wind farm in California.