Multi-step Intelligent Forecasting Method for Electricity Demand of Fused Magnesia Production
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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.Abstract: 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 coordination. 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.
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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)$
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) $
$\hat{\bar{r} }_i(k)-\hat{\bar{r} }_i(k-1)\geq 0$ $\hat{\bar{r} }_i(k)-\hat{\bar{r} }_i(k-1) < 0$ $\bar{r}_i(k)-\bar{r}_i(k-1)\geq 0$ ${TP}_i(k)=1$ ${FP}_i(k)=1$ $\bar{r}_i(k)-\bar{r}_i(k-1)< 0$ ${FN}_i(k)=1$ ${TN}_i(k)=1$ 
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表 2 需量预报精度对比
Table 2 Precision comparison of demand forecast
预报步数$i$ 1 2 3 4 5 6 7 8 9 10 $R^2_i\;(\%)$ 本文 99.96 99.62 99.59 99.47 99.39 99.31 98.99 98.51 98.03 97.95 文献[9] 90.34 90.05 89.77 89.54 88.73 88.48 88.01 87.76 87.33 86.94 ${{RMSE} }_i$ 本文 9.93 11.06 11.99 13.03 13.89 14.73 16.05 16.83 17.93 18.78 文献[9] 24.92 30.01 34.49 39.99 44.79 50.23 54.93 60.05 65.32 70.64 ${{MAPE} }_i\;(\%)$ 本文 0.04 0.05 0.05 0.06 0.06 0.07 0.07 0.08 0.08 0.08 文献[9] 0.11 0.13 0.15 0.18 0.20 0.22 0.24 0.27 0.29 0.32 $TPR_i\;(\%)$ 本文 94.88 93.21 92.19 91.42 90.17 89.77 88.21 90.05 91.55 89.66 文献[9] 86.12 82.11 80.05 80.11 78.94 79.33 79.11 77.06 80.15 79.02 $TNR_i\;(\%)$ 本文 93.22 94.67 92.19 92.01 94.21 93.18 90.96 89.99 88.12 90.01 文献[9] 81.12 80.04 80.67 83.72 79.99 80.15 77.56 86.77 80.15 76.91
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