Multi-step Intelligent Forecasting Method for Electricity Demand of Fused Magnesia Production

The electricity demand in a fused magnesia smelting process (FMSP) may first rise and then fall gradually, a phenomenon called demand peak. The fused magnesia furnace (FMF) will be switched off when the demand peak value reaches the limit. In order to avoid unnecessary FMF switching-off at the demand peak, it is necessary to identify the demand peak and predict next multi-step demand. In this paper, we develop a multi-step ahead demand forecasting model of the electricity demand based on the closed-loop control system of the smelting current in the FMSP. The multi-step ahead demand forecasting model combines an identifiable linear model with an unknown nonlinear dynamic system. A multi-step intelligent forecast method for electricity demand in the FMSP is proposed based on the system identification and deep learning with the edge-cloud structure. The experimental results using real data of the FMSP in a fused magnesia factory verify that the proposed method can effectively predict the trend of demand.