Deep interval prediction model with gradient descend optimization method for short-term wind power prediction
This work addresses a domain-specific bottleneck for power grid operators by improving the efficiency and accuracy of short-term wind power prediction, though it is incremental as it builds on existing LUBE frameworks.
The paper tackled the problem of inefficient and ineffective training in wind power interval prediction by proposing a deep interval prediction model with gradient descent optimization, resulting in a 45% improvement in prediction quality and a 66% reduction in time consumption compared to traditional methods.
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.