The short-term interval prediction of wind power using the deep learning model with gradient descend optimization. (August 2020) - Li, Chaoshun; Tang, Geng; Xue, Xiaoming; Chen, Xinbiao; Wang, Ruoheng; Zhang, Chu
Abstract: The application of wind power interval prediction for power systems attempts to give more comprehensive support to dispatchers and operators of the grid. Lower upper bound estimation (LUBE) method is widely applied in interval prediction. However, the existing LUBE approaches are trained by meta-heuristic optimization, which is either time-consuming or show poor effect when the LUBE model is complex. In this paper, a deep interval prediction method is designed in the framework of LUBE and an efficient gradient descend (GD) training approach is proposed to train the LUBE model. In this method, the long short-term memory is selected as a representative to show the modelling approach. The architecture of the proposed model consists of three parts, namely the long short-term memory module, the fully connected layers and the rank ordered module. Two loss functions are specially designed for implementing the GD training method based on the root mean square back propagation algorithm. To verify the performance of the proposed model, conventional LUBE models, as well as popular statistic interval prediction models are compared in numerical experiments. The results show that the proposed approach performs best in terms of effectiveness and efficiency with average 45% promotion in quality of prediction interval and 66% reduction of time consumptions compared to traditional LUBE models. Highlights: A new deep LUBE model based on gradient descend optimization method is proposed for the first time. Loss functions are designed to apply the root mean square back propagation method for effective model training. An improved coverage width criterion index is proposed to effectively evaluate interval prediction model. The superiority of the proposed method has been fully confirmed by 45% average performance promotion over its competitors.