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电熔镁砂生产用电需量多步智能预报方法

张菁雯 柴天佑 李慷

张菁雯, 柴天佑, 李慷. 电熔镁砂生产用电需量多步智能预报方法. 自动化学报, 2023, 49(9): 1868−1877 doi: 10.16383/j.aas.c220659
引用本文: 张菁雯, 柴天佑, 李慷. 电熔镁砂生产用电需量多步智能预报方法. 自动化学报, 2023, 49(9): 1868−1877 doi: 10.16383/j.aas.c220659
Zhang Jing-Wen, Chai Tian-You, Li Kang. Multi-step intelligent forecasting method for electricity demand of fused magnesia production. Acta Automatica Sinica, 2023, 49(9): 1868−1877 doi: 10.16383/j.aas.c220659
Citation: Zhang Jing-Wen, Chai Tian-You, Li Kang. Multi-step intelligent forecasting method for electricity demand of fused magnesia production. Acta Automatica Sinica, 2023, 49(9): 1868−1877 doi: 10.16383/j.aas.c220659

电熔镁砂生产用电需量多步智能预报方法

doi: 10.16383/j.aas.c220659
基金项目: 2020年度辽宁省科技重大专项计划(2020JH1/10100008), 国家自然科学基金重大项目 (61991404), 一体化过程控制学科创新引智基地 2.0 (B08015), 国家重点研发计划(2019YFB2006202) 资助
详细信息
    作者简介:

    张菁雯: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. 2016年获得大连理工大学硕士学位. 主要研究方向为深度学习, 工业人工智能. E-mail: 1610277@stu.neu.edu.cn

    柴天佑:中国工程院院士, 东北大学教授. IEEE Life Fellow, IFAC Fellow, 欧亚科学院院士. 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与智能化系统理论、方法与技术. 本文通信作者. E-mail: tychai@mail.neu.edu.cn

    李慷:利兹大学教授, 通讯及电力网络研究所所长. 1995年获上海交通大学博士学位. 先后在上海交通大学、代尔夫特理工大学、女王大学工作. 主要研究方向为系统建模, 人工智能, 以及在电力能源、制造及交通等领域的工程应用. E-mail: K.Li1@leeds.ac.uk

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

Funds: Supported by 2020 Science and Technology Major Project of Liaoning Province (2020JH1/10100008), Major Program of National Natural Science Foundation of China (61991404), 111 Project 2.0 (B08015), and National Key Research and Development Program (2019YFB2006202)
More Information
    Author Bio:

    ZHANG Jing-Wen Ph.D. candidate at the State Key Laboratory of Synthetical Automation for Process Industries of Northeastern University. She received her master degree from Dalian University of Technology in 2016. Her research interest covers deep learning and industrial artificial intelligence

    CHAI Tian-You Academician of Chinese Academy of Engineering, professor at Northeastern University, IEEE Life Fellow, IFAC Fellow, and academician of the International Eurasian Academy of Sciences. His research interest covers adaptive control, intelligent decoupling control, and theories, methods and technology of synthetical automation and intelligent system for process industries. Corresponding author of this paper

    LI Kang Professor and director at the Institute of Communication and Power Networks, University of Leeds. He received his Ph.D. degree from Shanghai Jiao Tong University in 1995. He successively worked in Shanghai Jiao Tong University, Delft University of Technology and Queen's University. His research interest covers systems modeling, artificial intelligence, and engineering applications in the fields of power energy, manufacturing, and transportation

  • 摘要: 电熔镁砂生产 (Fused magnesia smelting process, FMSP)用电需量会出现先升后降的尖峰现象, 当峰值达到用电需量限幅值, 会将电熔镁炉(Fused magnesia furnace, FMF)拉闸断电. 为避免尖峰时刻的不必要拉闸需要对需量尖峰进行识别, 因此需要进行需量多步预报. 利用电熔镁砂生产过程熔化电流闭环控制系统方程建立了由线性模型和未知非线性动态系统组成的需量多步预报模型, 将系统辨识与深度学习相结合提出了端边云协同的电熔镁砂生产用电需量多步智能预报方法. 采用电熔镁砂生产过程的工业大数据的实验结果验证了所提的预报方法可以准确预报需量的变化趋势.
  • 图  1  电熔镁砂生产用电需量监控流程图

    Fig.  1  An flow chart of electricity demand monitoring process for a fused magnesia production

    图  2  端边云协同需量多步智能预报结构图

    Fig.  2  Schematic of multi-step intelligent forecasting of demand with edge-cloud coordination

    图  3  $ \bar{r}(k+i)$的深度学习预报模型结构

    Fig.  3  Structure of deep learning prediction model of $ \bar{r}(k+i)$

    图  4  需量1-步预报结果

    Fig.  4  Demand forecast results for the 1st-step

    图  5  需量5-步预报结果

    Fig.  5  Demand forecast results for the 5th-step

    图  6  需量10-步预报结果

    Fig.  6  Demand forecast results for the 10th-step

    表  1  $ { TP}_i(k), { FP}_i(k), { TN}_i(k), {FN}_i(k)$的计算方式

    Table  1  Formula mode of ${ TP}_i(k), { FP}_i(k), $ ${ TN}_i(k), {FN}_i(k) $

    $\hat{\bar{r} }_i(k)-\hat{\bar{r} }_i(k-1)\geq 0$$\hat{\bar{r} }_i(k)-\hat{\bar{r} }_i(k-1) < 0$
    $\bar{r}_i(k)-\bar{r}_i(k-1)\geq 0$${TP}_i(k)=1$${FP}_i(k)=1$
    $\bar{r}_i(k)-\bar{r}_i(k-1)< 0$${FN}_i(k)=1$${TN}_i(k)=1$
    下载: 导出CSV

    表  2  需量预报精度对比

    Table  2  Precision comparison of demand forecast

    预报步数$i$12345678910
    $R^2_i\;(\%)$本文99.9699.6299.5999.4799.3999.3198.9998.5198.0397.95
    文献[9]90.3490.0589.7789.5488.7388.4888.0187.7687.3386.94
    ${{RMSE} }_i$本文9.9311.0611.9913.0313.8914.7316.0516.8317.9318.78
    文献[9]24.9230.0134.4939.9944.7950.2354.9360.0565.3270.64
    ${{MAPE} }_i\;(\%)$本文0.040.050.050.060.060.070.070.080.080.08
    文献[9]0.110.130.150.180.200.220.240.270.290.32
    $TPR_i\;(\%)$本文94.8893.2192.1991.4290.1789.7788.2190.0591.5589.66
    文献[9]86.1282.1180.0580.1178.9479.3379.1177.0680.1579.02
    $TNR_i\;(\%)$本文93.2294.6792.1992.0194.2193.1890.9689.9988.1290.01
    文献[9]81.1280.0480.6783.7279.9980.1577.5686.7780.1576.91
    下载: 导出CSV
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  • 收稿日期:  2022-08-20
  • 录用日期:  2022-12-13
  • 网络出版日期:  2023-02-13
  • 刊出日期:  2023-09-26

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