Energy consumption prediction for steelmaking production using PSO-based BP neural network
Yanyan Zhang, Shengnan Zhao, Lixin Tang · 2016
This paper deals with the energy consumption prediction in steel industry using particle swarm optimization-based back propagation Neural Network. More than ten types of energy including electricity, coal, power and gas are considered simultaneously. The problem is further complicated by the consumption, regeneration and conversion of energy. The objective is to estimate as accurately as possible the amount of energy to be consumed for steelmaking operation in future production horizon. The improved neural network algorithm is designed by introducing momentum term, adaptive learning rate and swarm intelligence. The test results of real data from a steel enterprise show that the proposed method outperforms the standard version back propagation with respect to prediction accuracy and running time.