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A novel hybrid data-driven model for multi-input single-output system simulation

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Abstract

Artificial neural network (ANN)-based data-driven model is an effective and robust tool for multi-input single-output (MISO) system simulation task. However, there are several conundrums which deteriorate the performance of the ANN model. These problems include the hard task of topology design, parameter training, and the balance between simulation accuracy and generalization capability. In order to overcome conundrums mentioned above, a novel hybrid data-driven model named KEK was proposed in this paper. The KEK model was developed by coupling the K-means method for input clustering, ensemble back-propagation (BP) ANN for output estimation, and K-nearest neighbor (KNN) method for output error estimation. A novel calibration method was also proposed for the automatic and global calibration of the KEK model. For the purpose of intercomparison of model performance, the ANN model, KNN model, and proposed KEK model were applied for two applications including the Peak benchmark function simulation and the real-world electricity system daily total load forecasting. The testing results indicated that the KEK model outperformed other two models and showed very good simulation accuracy and generalization capability in the MISO system simulation tasks.

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Acknowledgments

This research was funded by the IWHR Research and Development Support Program (JZ0145B052016) IWHR Scientific Research Projects of Outstanding Young Scientists “Research and application on the fast global optimization method for the Xinanjiang model parameters based on the high performance heterogeneous computing” (No. KY1605), Specific Research of China Institute of Water Resources and Hydropower Research (Grant Nos. Fangji 1240), the Third Sub-Project: Flood Forecasting, Controlling and Flood Prevention Aided Software Development—Flood Control Early Warning Communication System and Flood Forecasting, Controlling and Flood Prevention Aided Software Development for Poyang Lake Area of Jiangxi Province (0628-136006104242, JZ0205A432013, SLXMB200902), Study of distributed flood risk forecast model and technology based on multi-source data integration and hydrometeorological coupling system (2013CB036400), IWHR application project of multi-source precipitation fusion and soil moisture remote sensing assimilation, the NNSF of China, Numerical Simulation Technology of Flash Flood based on Godunov Scheme and Its Mechanism Study by Experiment (No. 51509263), and China Postdoctoral Science Foundation on Grant (Grant NO.: 2016M591214). We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Tesla K40 GPU used for this research.

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Authors and Affiliations

  1. State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, Research Center on Flood and Drought Disaster Reduction of the Ministry of Water Resources, China Institute of Water Resources and Hydropower Research, Beijing, 100038, People’s Republic of China

    Guangyuan Kan, Xiaoyan He, Jiren Li, Liuqian Ding, Dawei Zhang & Tianjie Lei

  2. State Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing, People’s Republic of China

    Guangyuan Kan & Yang Hong

  3. Department of Civil Engineering and Environmental Science, University of Oklahoma, Norman, OK, USA

    Yang Hong

  4. College of Hydrology and Water Resources, Hohai University, Nanjing, 210098, People’s Republic of China

    Ke Liang

  5. College of Water Sciences, Beijing Normal University, Beijing, 100875, People’s Republic of China

    Depeng Zuo

  6. Nanjing Hydraulic Research Institute, Nanjing, 210029, People’s Republic of China

    Zhenxin Bao

  7. State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, Department of Water Resources (DWR), China Institute of Water Resources and Hydropower Research, Beijing, 100038, People’s Republic of China

    Mengjie Zhang

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  1. Guangyuan Kan
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  2. Xiaoyan He
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  3. Jiren Li
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  4. Liuqian Ding
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  5. Dawei Zhang
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  6. Tianjie Lei
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  7. Yang Hong
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  8. Ke Liang
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  9. Depeng Zuo
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  10. Zhenxin Bao
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  11. Mengjie Zhang
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Corresponding author

Correspondence to Dawei Zhang.

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Kan, G., He, X., Li, J. et al. A novel hybrid data-driven model for multi-input single-output system simulation. Neural Comput & Applic 29, 577–593 (2018). https://doi.org/10.1007/s00521-016-2534-y

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  • DOI: https://doi.org/10.1007/s00521-016-2534-y

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