Short-Term Wind Speed Forecasting Using Regularization Extreme Learning Machine

Abstract

High efficient and accurate wind speed prediction is the basis of the wind farm power prediction. So it is helpful for the control of the wind power and has great importance to the parallel operation of the wind farms. Wind speed time series has strong nonlinear and volatility. Besides, it is very difficult to accurately predict. A new method of short-term wind speed forecasting is proposed based on regularized extreme learning machine (regularized extreme learning machine, RELM). First of all, the autocorrelation function (ACF) is used to analyze the correlation of wind speed time series. After that the number embedded in the time dimension is gotten. And the forecast network parameters such as inputs, output and so on are determined. That is to say the RELM model is set up. Then, using the training set trains the network parameters to get the RELM prediction model trained. Finally, prediction results are obtained with the test set data. And the wind speed data from the American wind energy technology center is carried out on the experiment. It shows that the new method has better prediction precision compared with the standard ELM and the traditional neural network.