A Novel Intelligent Deep Learning-Based Uncertainty-Guided Network Training in Market Price

Abstract

In this article, we introduce an intelligent probabilistic uncertainty-guided prediction model for energy market price modeling based on the lower–upper bound estimation method. The proposed method borrows the prediction interval (PI) concept to replace the traditional point–point estimation process. Making use of the PI, the forecast error would be captured within the upper and lower bounds of the PI. The proposed method uses the long–short term networks to learn the complex and nonlinear dataset of market prices with high accuracy. Considering the conflicting nature of the prediction indices, a fuzzy min–max solution is introduced to get into a compromised solution. From the optimization point of view, a new method based on the grey wolf algorithm (GWA) is proposed to adjust the model hyperparameters suitably. Moreover, a new modification is introduced to increase the diversity of the GWA and, thus, enhance its search capability. To validate the proposed model, a standard dataset from the New South Wales electricity market is used to assess the high accuracy of the model to capture the prediction error.