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Analysis of energy and control efficiencies of fuzzy logic and artificial neural network technologies in the heating energy supply system responding to the changes of user demands

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  • Ahn, Jonghoon
  • Cho, Soolyeon
  • Chung, Dae Hun

Abstract

This paper presents hybrid control approaches for heating air supply in response to changes in demand by using the Fuzzy Inference System (FIS) and Artificial Neural Network (ANN) fitting models.

Suggested Citation

  • Ahn, Jonghoon & Cho, Soolyeon & Chung, Dae Hun, 2017. "Analysis of energy and control efficiencies of fuzzy logic and artificial neural network technologies in the heating energy supply system responding to the changes of user demands," Applied Energy, Elsevier, vol. 190(C), pages 222-231.
    • Handle: RePEc:eee:appene:v:190:y:2017:i:c:p:222-231
    DOI: 10.1016/j.apenergy.2016.12.155
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    4. Ahmad Esmaeilzadeh & Brian Deal & Aghil Yousefi-Koma & Mohammad Reza Zakerzadeh, 2022. "How Multi-Criterion Optimized Control Methods Improve Effectiveness of Multi-Zone Building Heating System Upgrading," Energies, MDPI, vol. 15(22), pages 1-27, November.
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    14. Jonghoon Ahn, 2021. "Abatement of the Increases in Cooling Energy Use during a Period of Intense Heat by a Network-Based Adaptive Controller," Sustainability, MDPI, vol. 13(3), pages 1-17, January.
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