数据与模型驱动的电熔镁群炉需量预报方法

杨杰; 柴天佑; 张亚军; 吴志伟

doi:10.16383/j.aas.2017.c160597

数据与模型驱动的电熔镁群炉需量预报方法

doi: 10.16383/j.aas.2017.c160597

杨杰^1, ,,
柴天佑^1,2,,
张亚军^1,,
吴志伟^1,

1.
东北大学流程工业综合自动化国家重点实验室沈阳 110819
2.
东北大学自动化研究中心沈阳 110819

基金项目:

国家自然科学基金 61403071

教育部项目基本科研业务费培育种子基金 N140804001

国家自然科学基金 61503066

中国博士后科学基金 2014M561246

中国博士后科学基金 2015M581355

辽宁省博士启动基金项目 201501151

教育部项目基本科研业务费培育种子基金 N160801001

详细信息

作者简介:
柴天佑  中国工程院院士, 东北大学教授, IEEE Fellow, IFAC Fellow, 欧亚科学院院士.1985年获得东北大学博士学位.主要研究方向为自适应控制, 智能解耦控制, 流程工业综合自动化理论、方法与技术.E-mail:tychai@mail.neu.edu.cn

张亚军  东北大学博士后.主要研究方向为非线性模糊自适应控制理论, 广义预测控制, 多模型切换控制, 智能解耦控制, 数据驱动控制, 智能控制系统的大数据建模, 工业过程大数据建模及其应用.E-mail:zhangyajun79@gmail.com

吴志伟  东北大学讲师.2015年获得东北大学博士学位.主要研究方向为复杂工业过程的运行控制和工业嵌入式控制系统开发.E-mail:wuzhiwei_2006@163.com

通讯作者:
杨杰东北大学流程工业综合自动化国家重点实验室博士研究生.主要研究方向为工业过程数据驱动建模技术及应用.本文通信作者.E-mail:yjercou@126.com

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出版历程
- 收稿日期: 2016-08-20
- 录用日期: 2017-02-03
- 刊出日期: 2018-08-20

Data and Model Driven Demand Forecasting Method for Fused Magnesium Furnace Group

YANG Jie^{1
, ,},
CHAI Tian-You^{1,2
,},
ZHANG Ya-Jun^1
,,
WU Zhi-Wei^1
,

1.
State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang 110819
2.
Research Center of Automation, Northeastern University, Shenyang 110819

Funds:

National Natural Science Foundation of China 61403071

Fundamental Research Funds for the Central Universities of Ministry of Education N140804001

National Natural Science Foundation of China 61503066

China Postdoctoral Science Foundation 2014M561246

China Postdoctoral Science Foundation 2015M581355

Doctoral Research Foundation of Liaoning Province 201501151

Fundamental Research Funds for the Central Universities of Ministry of Education N160801001

More Information

Author Bio:
Academician of Chinese Academy of Engineering, professor at Northeastern University, IEEE Fellow, IFAC Fellow. He received his Ph. D. degree from Northeastern University in 1985. His research interest covers adaptive control, intelligent decoupling control, and integrated automation theory, method and technology of industrial process

Postdoctoral at Northeastern University. His research interest covers nonlinear fuzzy adaptive control theory, generalized predictive control, multiple models and switching systems, intelligent decoupling control, data-based driven control, big data-driven modelling theory, method and technology of intelligent control systems and process industries, and their applications

Lecturer at Northeastern University. He received his Ph. D. degree from Northeastern University in 2015. His current research interest covers operational control for complex industry process and industrial embedded control system

Corresponding author: YANG Jie Ph. D. candidate at the State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University. His research interest covers data driven modeling technology and application for industrial process. Corresponding author of this paper

摘要

摘要: 电熔镁群炉需量指当前时刻k和（k-1），…，（k-n+1）时刻群炉功率的平均值，用于度量高耗能电熔镁群炉用电量.（k+1）时刻群炉需量取决于功率变化率.本文建立了功率变化率与电流控制系统输出电流之间由线性项与未知非线性项组成的动态模型，其中线性项通过电流被控对象的参数和控制器的参数计算，未知非线性项采用基于偏自相关函数（Partial autocorrelation function，PACF）输入变量决策的径向基函数神经网络（Radial basis function neural network，RBFNN）来估计.本文提出了由当前k时刻的需量和功率，（k-n+1）时刻功率及k时刻功率变化率的估计组成的（k+1）时刻需量的计算模型.通过某电熔镁砂厂实际数据的仿真实验和工业实验表明所提方法可准确预报需量变化趋势，可以防止因原料变化引起需量尖峰导致错误切断电熔镁炉供电造成电熔镁砂质量降低.
- 需量预报 /
- 电熔镁群炉 /
- 数据与模型驱动 /
- 径向基函数神经网络
Abstract: The demand of fused magnesium furnace group (FMFG) is the average value of powers at times k, (k-1), …, (k-n+1). The demand indicates the electricity consumption of the FMFG. The demand at time (k+1) depends on the rate of power change. In this paper, we develop a dynamic model of the rate of power change and the output current. The model consists of a linear term and an unknown nonlinear term, where the linear term can be calculated by the parameters of the controlled current and the controller, and the unknown nonlinear term can be estimated using the radial basis function neural network (RBFNN). The input variables of RBFNN are decided based on partial autocorrelation function (PACF). Then a computing model of demand at time (k+1) is proposed, which consists of the demand at time k, the powers at times k and (k-n+1) and the estimate of the rate of power change at time k. Simulations based on actual data and industrial experiments at a fused magnesia plant show that the proposed method can accurately forecast demand trends and can prevent reduction of fused magnesia grade caused by unnecessary cut off due to the demand spikes caused by change of raw materials.
- Demand forecasting /
- fused magnesium furnace group (FMFG) /
- data and model driven /
- radial basis function neural network (RBFNN)
注释:

1) 本文责任编委张卫东

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本文责任编委张卫东

图 1 电熔镁群炉需量监控原理图

Fig. 1 Schematic diagram of demand monitoring process for FMFG

下载: 全尺寸图片幻灯片

图 2 电熔镁群炉需量预报方法结构框图

Fig. 2 The structure diagram of demand forecasting method for FMFG

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图 3 隐层节点数的交叉验证

Fig. 3 Cross-validation of the number of hidden nodes

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图 4 高斯函数宽度的交叉验证

Fig. 4 Cross-validation of the width of Gaussian function

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图 5 ∆p(k)预报验证曲线

Fig. 5 Forecast validation curves of ∆p(k)

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图 6 时段1需量曲线

Fig. 6 Demand curve for the 1st time period

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图 7 时段2需量曲线

Fig. 7 Demand curve for the 2nd time period

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图 8 时段3需量曲线

Fig. 8 Demand curve for the 3rd time period

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图 9 需量预报软件界面

Fig. 9 The interface of demand forecasting software

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图 10 时段1需量曲线

Fig. 10 Demand curve for the 1st time period

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图 11 时段2需量曲线

Fig. 11 Demand curve for the 2nd time period

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图 12 时段3需量曲线

Fig. 12 Demand curve for the 3rd time period

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图 13 超限拉闸时段需量曲线

Fig. 13 Demand curve for cut off time period

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图 14 超限拉闸时段需量预报误差变化曲线

Fig. 14 Demand forecast error curve during cut off time period

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图 15 恢复供电动作下的需量预报

Fig. 15 Demand forecast curve during restore operations

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图 16 恢复供电动作下的需量预报误差

Fig. 16 Demand forecast error curve during restore operations

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图 17 工业实验需量预报误差白度分析

Fig. 17 The whiteness analysis of demand forecast error in industrial experiment

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表 1 ∆p(k)预报误差指标

Table 1 Forecast error indicators of ∆p(k)

方差	RMSE
1.1481E+6	1 071.3

下载: 导出CSV

表 2 需量预报误差指标

Table 2 Forecast error indicators of demand

方法	方差	PB (%)	RMSE	MAPE (%)
文献[5]	1 533.0	97.05	39.1921	0.0979
本文	1 275.7	97.55	35.7104	0.1054

下载: 导出CSV

表 3 超限拉闸时段需量预报误差

Table 3 Demand forecast errors during cut off time period

时间	需量实际值(kW)	需量预报值(kW)	误差(kW)
22 : 36 : 50	21 626	21 635	-9
22 : 36 : 57	21 654	21 650	4
22 : 37 : 04	21 692	21 685	7
22 : 37 : 11	21 728	21 725	3
22 : 37 : 18	21 754	21 751	4
22 : 37 : 25	21 788	21 787	1
22 : 37 : 32	21 812	21 829	-17
22 : 37 : 39	21 834	21 849	-15
22 : 37 : 46	21 835	21 839	-4
22 : 37 : 53	21 833	21 829	4
22 : 38 : 00	21 826	21 822	4
22 : 38 : 07	21 691	21 819	-128
22 : 38 : 14	21 510	21 463	47
22 : 38 : 21	21 335	21 318	17
22 : 38 : 28	21 191	21 176	15
22 : 38 : 35	21 049	21 056	-7
22 : 38 : 42	20 908	20 907	1
22 : 38 : 49	20 751	20 769	-18
22 : 38 : 56	20 581	20 577	4
22 : 39 : 03	20 407	20 385	22

下载: 导出CSV

表 4 工业实验需量预报误差指标

Table 4 Demand forecast error indicators of industrial experiment

方差	PB (%)	RMSE	MAPE
1 049.8	97.68	32.3981	0.0996

下载: 导出CSV

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数据与模型驱动的电熔镁群炉需量预报方法

doi: 10.16383/j.aas.2017.c160597

通讯作者:
杨杰东北大学流程工业综合自动化国家重点实验室博士研究生.主要研究方向为工业过程数据驱动建模技术及应用.本文通信作者.E-mail:yjercou@126.com

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Data and Model Driven Demand Forecasting Method for Fused Magnesium Furnace Group

Corresponding author: YANG Jie Ph. D. candidate at the State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University. His research interest covers data driven modeling technology and application for industrial process. Corresponding author of this paper

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数据与模型驱动的电熔镁群炉需量预报方法

doi: 10.16383/j.aas.2017.c160597

通讯作者: 杨杰 东北大学流程工业综合自动化国家重点实验室博士研究生.主要研究方向为工业过程数据驱动建模技术及应用.本文通信作者.E-mail:yjercou@126.com

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Data and Model Driven Demand Forecasting Method for Fused Magnesium Furnace Group

Corresponding author: YANG Jie Ph. D. candidate at the State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University. His research interest covers data driven modeling technology and application for industrial process. Corresponding author of this paper

期刊类型引用(15)

其他类型引用(22)

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出版历程

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通讯作者:
杨杰东北大学流程工业综合自动化国家重点实验室博士研究生.主要研究方向为工业过程数据驱动建模技术及应用.本文通信作者.E-mail:yjercou@126.com