  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.
