Demand Forecasting of the Fused Magnesia Smelting Process With System Identification and Deep Learning
Tianyou Chai, Jingwen Zhang, Tao Yang
Abstract
The electricity demand of the fused magnesia smelting process (FMSP) is defined as the average electric power consumption over a fixed period of time, which is used to monitor the electricity cost in the FMSP. In this article, we develop a dynamic model of the electricity demand based on the closed-loop control system of the smelting current in the FMSP. The electricity demand prediction model combines an identifiable linear model with an unknown nonlinear dynamic system, which takes advantage of system identification. To predict the unknown nonlinear dynamic system, an adaptive deep learning prediction approach is proposed based on a multilayer long short-term memory. The real data in the FMSP is used to verify the effectiveness of the proposed electricity demand forecasting method.