Intelligent Demand Forecasting of Smelting Process Using Data-Driven and Mechanism Model

Abstract

The demand for electricity for the fused magnesia smelting process is calculated by the moving average of the energy consumption of a given period, which gives the efficiency of the smelting and indicates if the power supply should be cut off. Therefore, demand forecasting is very important for the operation of the smelting process. However, it is difficult to forecast demand since the power changes depend on the smelting process and raw materials. To obtain an accurate model, the mechanism model of the smelting process is combined with the data-driven method using artificial intelligence technology in this paper. The mechanism model is described by a linear model with unknown parameters. The uncertainties and error of the mechanism model are modeled by neural networks with unknown order. The maximal information coefficient method and the rule reasoning are combined to identify the order. To effectively combine the mechanism model and the data-driven model, a new saturated alternating identification strategy is proposed. The results of simulations and industrial applications show that the effectiveness of the proposed intelligent method of demand forecasting has been validated.