Efficient prediction for Blast Furnace Gas holder level using novel preprocessing techniques and weight correction strategy

Abstract

Accurately forecasting the level of Blast Furnace Gas (BFG) holder, managing the balance of gas supply and demand, strategically allocating BFG brings substantial economic and social benefits. In order to address the issues of volatile fluctuation, strong temporal variability, and high uncertainty of BFG, this paper proposes an efficient system for predicting the BFG holder level. Variational Mode Decomposition is utilized to separate components with different frequencies. The number of modes is determined according to Energy Ratio. Improved Discrete Wavelet Threshold is employed to eliminate noise. The grid search approach adjusts the activation functions. What is more, Particle Swarm Optimization is used to correct the weights of subsequences and emphasize critical information. Experimental results demonstrate that Root Mean Squared Error of the system are 0.0411, 0.0423 and 0.0416 for the three real-time monitoring datasets in April 2021. The average improvement percentage for the second dataset is 67.5% when compared to other commonly models. It is obvious that the prediction errors are minimized, and the stability and generalizability of the hybrid prediction system are enhanced. Additionally, the performance of the system is further verified through Diebold–Mariano test, improvement percentage test, and effectiveness test.