Demand Forecasting of the Fused Magnesia Smelting Process With System Identification and Deep Learning

被引：42

作者：

Chai, Tianyou ^{[1
]}

Zhang, Jingwen ^{[1
]}

Yang, Tao ^{[1
]}

机构：

[1] Northeastern Univ, State Key Lab Synthet Automat Proc Ind, Shenyang 110819, Peoples R China

来源：

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS | 2021年 / 17卷 / 12期

基金：

国家自然科学基金重大项目;

关键词：

Smelting; Nonlinear dynamical systems; Monitoring; Predictive models; Demand forecasting; Deep learning; Process control; Adaptive deep learning; demand forecasting; long short-term memory (LSTM); unknown nonlinear dynamic system; SHORT-TERM LOAD; ELECTRICITY DEMAND; NEURAL-NETWORK; PREDICTION;

D O I：

10.1109/TII.2021.3065930

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：8387 / 8396

页数：10

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