Iron and steel process energy consumption prediction model based on selective ensemble

Aiming at solving the energy consumption and pollution problem of China´s steel industry, this paper proposed an energy consumption prediction model by using intelligence algorithms based on selective ensemble theory. The main work of this paper is realizing the prediction model through BP artificial neural network and genetic algorithm, moreover, combining the 2 methods mentioned above to improve accuracy. Based on the same dataset, this paper compared the experimental result with single intelligence algorithm, and it indicates that selective ensemble could enhance the weak learning algorithm to a strong one, so that to improve the prediction accuracy. This method has achieved a relatively good effect in energy prediction by adjusting coking charge ratio, meanwhile provided guidance for iron enterprise in energy saving and emission reduction.