Gaussian mixture model coupled recurrent neural networks for wind speed interval forecast

The potential of long short-term memory network on ultra-short term wind speed forecast attracted attentions of researchers in recent years. Extending a probabilistic long short-term memory network model to provide an uncertainty estimation than to make a point forecast is more valuable in practice. However, due to complex recurrent structure and feedback algorithm, large scale ensemble forecast based on resampling faces great challenges in reality. Instead, a reliable forecast method needs to be devised. Gaussian process regression is a probabilistic regression model based on Gaussian Process prior. It is reasonable to integrate Gaussian process regression with long short-term memory network for probabilistic wind speed forecast to leverage the superior fitting ability of the deep learning methods and to maintain the probability characteristics of Gaussian process regression. Hence, avoid the repeated training and heavy parameter optimization. The method is evaluated for wind speed forecast using the monitoring dataset provided by the National Wind Energy Technology Center. The results indicated that the proposed method improves the point forecast accuracy by up to 17.2%, and improves the interval forecast accuracy by up to 18.5% compared to state-of-the-art models. This study is of great significance for improving the accuracy and reliability of wind speed prediction and the sustainable development of new energy sources.