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Ensemble of Regression-Type and Interpolation-Type Metamodels

Author

Listed:
  • Cheng Yan

    (School of Aerospace Engineering, Xiamen University, Xiamen 361005, China)

  • Jianfeng Zhu

    (School of Aerospace Engineering, Xiamen University, Xiamen 361005, China)

  • Xiuli Shen

    (School of Energy and Power Engineering, Beihang University, Beijing 100191, China)

  • Jun Fan

    (Army Aviation Institute, Beijing 100000, China)

  • Dong Mi

    (AECC Hunan Aviation Powerplant Research Institute, Zhuzhou 412002, China)

  • Zhengming Qian

    (AECC Hunan Aviation Powerplant Research Institute, Zhuzhou 412002, China)

Abstract

Metamodels have become increasingly popular in the field of energy sources because of their significant advantages in reducing the computational cost of time-consuming tasks. Lacking the prior knowledge of actual physical systems, it may be difficult to find an appropriate metamodel in advance for a new task. A favorite way of overcoming this difficulty is to construct an ensemble metamodel by assembling two or more individual metamodels. Motivated by the existing works, a novel metamodeling approach for building the ensemble metamodels is proposed in this paper. By thoroughly exploring the characteristics of regression-type and interpolation-type metamodels, some useful information is extracted from the feedback of the regression-type metamodels to further improve the functional fitting capability of the ensemble metamodels. Four types of ensemble metamodels were constructed by choosing four individual metamodels. Common benchmark problems are chosen to compare the performance of the individual and ensemble metamodels. The results show that the proposed metamodeling approach reduces the risk of selecting the worst individual metamodel and improves the accuracy of the used individual metamodels.

Suggested Citation

  • Cheng Yan & Jianfeng Zhu & Xiuli Shen & Jun Fan & Dong Mi & Zhengming Qian, 2020. "Ensemble of Regression-Type and Interpolation-Type Metamodels," Energies, MDPI, vol. 13(3), pages 1-20, February.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:3:p:654-:d:316081
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    References listed on IDEAS

    as
    1. Wei-Chiang Hong & Guo-Feng Fan, 2019. "Hybrid Empirical Mode Decomposition with Support Vector Regression Model for Short Term Load Forecasting," Energies, MDPI, vol. 12(6), pages 1-16, March.
    2. Bornatico, Raffaele & Hüssy, Jonathan & Witzig, Andreas & Guzzella, Lino, 2013. "Surrogate modeling for the fast optimization of energy systems," Energy, Elsevier, vol. 57(C), pages 653-662.
    3. Jesús Ferrero Bermejo & Juan Francisco Gómez Fernández & Rafael Pino & Adolfo Crespo Márquez & Antonio Jesús Guillén López, 2019. "Review and Comparison of Intelligent Optimization Modelling Techniques for Energy Forecasting and Condition-Based Maintenance in PV Plants," Energies, MDPI, vol. 12(21), pages 1-18, October.
    4. Seungbeom Nam & Jin Hur, 2018. "Probabilistic Forecasting Model of Solar Power Outputs Based on the Naïve Bayes Classifier and Kriging Models," Energies, MDPI, vol. 11(11), pages 1-15, November.
    5. Qi Zhou & Youmin Rong & Xinyu Shao & Ping Jiang & Zhongmei Gao & Longchao Cao, 2018. "Optimization of laser brazing onto galvanized steel based on ensemble of metamodels," Journal of Intelligent Manufacturing, Springer, vol. 29(7), pages 1417-1431, October.
    6. González-Fernández, Cristina & Molinuevo-Salces, Beatriz & García-González, Maria Cruz, 2011. "Evaluation of anaerobic codigestion of microalgal biomass and swine manure via response surface methodology," Applied Energy, Elsevier, vol. 88(10), pages 3448-3453.
    7. Arridina Susan Silitonga & Teuku Meurah Indra Mahlia & Abd Halim Shamsuddin & Hwai Chyuan Ong & Jassinnee Milano & Fitranto Kusumo & Abdi Hanra Sebayang & Surya Dharma & Husin Ibrahim & Hazlina Husin , 2019. "Optimization of Cerbera manghas Biodiesel Production Using Artificial Neural Networks Integrated with Ant Colony Optimization," Energies, MDPI, vol. 12(20), pages 1-21, October.
    8. Qi Zhou & Longchao Cao & Hui Zhou & Xiang Huang, 2018. "Prediction of angular distortion in the fiber laser keyhole welding process based on a variable-fidelity approximation modeling approach," Journal of Intelligent Manufacturing, Springer, vol. 29(3), pages 719-736, March.
    9. Xinyu An & Baowei Song & Zhaoyong Mao & Congcong Ma, 2018. "Layout Optimization Design of Two Vortex Induced Piezoelectric Energy Converters (VIPECs) Using the Combined Kriging Surrogate Model and Particle Swarm Optimization Method," Energies, MDPI, vol. 11(8), pages 1-22, August.
    10. Cheng-Wen Lee & Bing-Yi Lin, 2017. "Applications of the Chaotic Quantum Genetic Algorithm with Support Vector Regression in Load Forecasting," Energies, MDPI, vol. 10(11), pages 1-18, November.
    11. Dan Wang & Qing’e Hu & Jia Tang & Hongjie Jia & Yun Li & Shuang Gao & Menghua Fan, 2017. "A Kriging Model Based Optimization of Active Distribution Networks Considering Loss Reduction and Voltage Profile Improvement," Energies, MDPI, vol. 10(12), pages 1-19, December.
    12. Jason Runge & Radu Zmeureanu, 2019. "Forecasting Energy Use in Buildings Using Artificial Neural Networks: A Review," Energies, MDPI, vol. 12(17), pages 1-27, August.
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