Deep Learning Assisted Online Multi-Step Demand Forecasting of Fused Magnesia Smelting Processes
This paper proposes a multi-step ahead power demand model for fused magnesia smelting processes (FMSP) which combines a linear model and an unknown nonlinear term to predict the electricity demand and its variation tendency for the next 5 steps. The linear model is identified by the multi-output fas...
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| Vydané v: | 2022 4th International Conference on Industrial Artificial Intelligence (IAI) s. 1 - 8 |
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24.08.2022
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| Abstract | This paper proposes a multi-step ahead power demand model for fused magnesia smelting processes (FMSP) which combines a linear model and an unknown nonlinear term to predict the electricity demand and its variation tendency for the next 5 steps. The linear model is identified by the multi-output fast recursive algorithm (MFRA) while the unknown nonlinear term is fitted with a long-short term memory (LSTM) model. The hyperparameters in the LSTM are estimated by the Bayesian optimization (BO) algorithm. Since the sampling period of the power is only 7 seconds, and we have to predict the next 5 steps electricity demand and its tendency within one sampling period, we therefore update parameters of the linear model by the MFRA while parameters of the dense layer of the LSTM are updated by the gradient descent algorithm within the online multi-step demand forecasting framework. The experimental results using the real-time data of a FMSP confirm the effectiveness of the proposed algorithm, achieving up to 52% error reduction in 5-step ahead demand forecasting when compared with other approaches. |
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| AbstractList | This paper proposes a multi-step ahead power demand model for fused magnesia smelting processes (FMSP) which combines a linear model and an unknown nonlinear term to predict the electricity demand and its variation tendency for the next 5 steps. The linear model is identified by the multi-output fast recursive algorithm (MFRA) while the unknown nonlinear term is fitted with a long-short term memory (LSTM) model. The hyperparameters in the LSTM are estimated by the Bayesian optimization (BO) algorithm. Since the sampling period of the power is only 7 seconds, and we have to predict the next 5 steps electricity demand and its tendency within one sampling period, we therefore update parameters of the linear model by the MFRA while parameters of the dense layer of the LSTM are updated by the gradient descent algorithm within the online multi-step demand forecasting framework. The experimental results using the real-time data of a FMSP confirm the effectiveness of the proposed algorithm, achieving up to 52% error reduction in 5-step ahead demand forecasting when compared with other approaches. |
| Author | Chai, Tianyou Li, Mingyu Zhang, Jingwen |
| Author_xml | – sequence: 1 givenname: Mingyu surname: Li fullname: Li, Mingyu email: 2010718@stu.neu.edu.cn organization: Northeastern University,State Key Laboratory of Synthetical Automation for Process Industries,Sheyang,China – sequence: 2 givenname: Jingwen surname: Zhang fullname: Zhang, Jingwen email: 1610277@stu.neu.edu.cn organization: Northeastern University,State Key Laboratory of Synthetical Automation for Process Industries,Sheyang,China – sequence: 3 givenname: Tianyou surname: Chai fullname: Chai, Tianyou email: tychai@mail.neu.edu.cn organization: Northeastern University,State Key Laboratory of Synthetical Automation for Process Industries,Sheyang,China |
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| Snippet | This paper proposes a multi-step ahead power demand model for fused magnesia smelting processes (FMSP) which combines a linear model and an unknown nonlinear... |
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| SubjectTerms | Bayesian optimization Deep learning Demand forecasting Long-short term memory Multi-output fast recursive algorithm Online multi-step ahead demand forecasting Power demand Prediction algorithms Predictive models Real-time systems Smelting |
| Title | Deep Learning Assisted Online Multi-Step Demand Forecasting of Fused Magnesia Smelting Processes |
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