Temporal convolutional networks interval prediction model for wind speed forecasting

• A promising wind speed interval prediction method with deep learning technique is proposed. • TCNs are employed to develop a neotype interval prediction method in LUBE framework. • An interval width adaptive adjustment strategy is designed to optimize the model by directly constructing training labels. • Experimental results demonstrate the superiority of the method on prediction accuracy and reliability by comparing with the state of art methods. Wind speed interval prediction is one of the most elusive and long-standing challenges in wind power production. As a data source with intermittent and fluctuant characteristics, wind speed time series require highly nonlinear temporal features for the prediction tasks. In this paper, a novel interval prediction model is proposed based on temporal convolutional networks to forecast wind speed. A temporal convolutional networks architecture layer, multiple fully connected layers using tanh activation function and an end-to-end sorting layer are respectively served as input, hidden and output layers of the temporal convolutional networks interval prediction model which can generate prediction intervals directly. Additionally, an adaptive interval construction optimization strategy is put forward to devise training labels for learning of model. Eight cases from two wind fields are implemented to test and verify the proposed method. Specially, experiments have been designed to compare the prediction accuracy and reliability between the proposed model and the most recent state-of-the-art models. The forecasting results suggest that the proposed model has a significant performance improvement on both prediction interval coverage probability and prediction interval width criteria and thus can be a practical tool for wind speed forecasting.