AdaBoosting Neural Network for Short-Term Wind Speed Forecasting Based on Seasonal Characteristics Analysis and Lag Space Estimation

摘要

High accurary in wind speed forcasting remains hard to achieve due to wind's random distribution nature and its seasonal characteristics. Randomness, intermittent and nonstationary usually cause the portion problem of the wind speed forecasting. Seasonal characteristics of wind speed means that its feature distribution is inconsistent. This typically results that the persistence of excitation for modeling can not be guaranteed, and may severely reduce the possibilities of high precise forecasting model. In this paper, we proposed two effective solutions to solve the problems caused by the randomness and seasonal characteristics of the wind speed. (1) Wavelet analysis is used to extract the robust components of time series and reduce the influence of randomness. (2) Based on the energy distribution about the extracted amplitude and associated frequency, seasonal characteristics of wind speed are analyzed based on self-similarity in periodogram under scales range generated by wavelet transformation. Thus, the original dataset is reasonably divided into subsest which can effectively reflect the seasonal distribution characteristics of wind speed. In addition, two strategies are given to optimal model structure and improve the forecasting accuracy: (1) The forecasting model's lag space is approximately estimated by the Lipschitz quotient to improve the generality ability of the feedforward neural network. (2) The forecasting accuracy and model robustness are further improved by the wavelet decomposition combined with AdaBoosting neural network. Finally, experimental evaluation based on the dataset from National Renewable Energy Laboratory (NREL) is given to demonstrate the performance of the proposed approach.