Optimal Uncertainty-guided Neural Network Training
This work addresses the need for more reliable and efficient uncertainty quantification in neural networks, particularly for applications like wind power generation and electricity demand forecasting, though it is incremental as it builds on existing methods.
The paper tackles the problem of non-convergence and suboptimal prediction intervals in neural network-based uncertainty quantification by proposing a highly customizable smooth cost function, resulting in improved convergence probability from 99.2% to 99.8% and accelerated training.
The neural network (NN)-based direct uncertainty quantification (UQ) methods have achieved the state of the art performance since the first inauguration, known as the lower-upper-bound estimation (LUBE) method. However, currently-available cost functions for uncertainty guided NN training are not always converging and all converged NNs are not generating optimized prediction intervals (PIs). Moreover, several groups have proposed different quality criteria for PIs. These raise a question about their relative effectiveness. Most of the existing cost functions of uncertainty guided NN training are not customizable and the convergence of training is uncertain. Therefore, in this paper, we propose a highly customizable smooth cost function for developing NNs to construct optimal PIs. The optimized average width of PIs, PI-failure distances and the PI coverage probability (PICP) are computed for the test dataset. The performance of the proposed method is examined for the wind power generation and the electricity demand data. Results show that the proposed method reduces variation in the quality of PIs, accelerates the training, and improves convergence probability from 99.2% to 99.8%.