Wind energy forecasting using multiple ARIMA models

Abstract

To achieve correct operation of wind farms, it is necessary to create accurate wind energy forecasting. Autoregressive integrated moving average (ARIMA) models were combined with artificial neural networks (NN) to obtain acceptable forecasting accuracy. But the forecasting results become worse when there are missing data or local minima in NN.In this paper, we use the multiple models and transfer-learning techniques to ARIMA. Since different wind farms have some similar features, we can use different ARIMA models and their wind farms’ data to get pre-training features . Then we do fine training by using the transfer-learning to combine these ARIMA models. This novel method can solve the low forecasting accuracy problems of ARIMA and NN. We successfully apply this method to wind energy forecasting. Experimental results show the forecasting accuracy of one wind farm is improved using the pre-trained models of the other two farms.