A novel hybrid data-driven model for multi-input single-output system simulation
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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.
KeywordsMISO system Data-driven model K-means method Ensemble BPANN KNN method KEK model
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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