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A Dynamic Fuzzy Controller to Meet Thermal Comfort by Using Neural Network Forecasted Parameters as the Input

Author

Listed:
  • Mario Collotta

    (Faculty of Engineering and Architecture, Kore University of Enna, Cittadella Universitaria, Enna 94100, Italy)

  • Antonio Messineo

    (Faculty of Engineering and Architecture, Kore University of Enna, Cittadella Universitaria, Enna 94100, Italy)

  • Giuseppina Nicolosi

    (Faculty of Engineering and Architecture, Kore University of Enna, Cittadella Universitaria, Enna 94100, Italy)

  • Giovanni Pau

    (Faculty of Engineering and Architecture, Kore University of Enna, Cittadella Universitaria, Enna 94100, Italy)

Abstract

Heating, ventilating and air-conditioning (HVAC) systems are typical non-linear time-variable multivariate systems with disturbances and uncertainties. In this paper, an approach based on a combined neuro-fuzzy model for dynamic and automatic regulation of indoor temperature is proposed. The proposed artificial neural network performs indoor temperatures forecasts that are used to feed a fuzzy logic control unit in order to manage the on/off switching of the HVAC system and the regulation of the inlet air speed. Moreover, the used neural network is optimized by the analytical calculation of the embedding parameters, and the goodness of this approach is tested through MATLAB. The fuzzy controller is driven by the indoor temperature forecasted by the neural network module and is able to adjust the membership functions dynamically, since thermal comfort is a very subjective factor and may vary even in the same subject. The paper shows some experimental results, through a real implementation in an embedded prototyping board, of the proposed approach in terms of the evolution of the inlet air speed injected by the fan coils, the indoor air temperature forecasted by the neural network model and the adjusting of the membership functions after receiving user feedback.

Suggested Citation

  • Mario Collotta & Antonio Messineo & Giuseppina Nicolosi & Giovanni Pau, 2014. "A Dynamic Fuzzy Controller to Meet Thermal Comfort by Using Neural Network Forecasted Parameters as the Input," Energies, MDPI, vol. 7(8), pages 1-30, July.
    • Handle: RePEc:gam:jeners:v:7:y:2014:i:8:p:4727-4756:d:38524
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    References listed on IDEAS

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    6. Jin Woo Moon & Jae D. Chang & Sooyoung Kim, 2013. "Determining Adaptability Performance of Artificial Neural Network-Based Thermal Control Logics for Envelope Conditions in Residential Buildings," Energies, MDPI, vol. 6(7), pages 1-23, July.
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    Cited by:

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    2. Ammar Hussein Mutlag & Azah Mohamed & Hussain Shareef, 2016. "A Nature-Inspired Optimization-Based Optimum Fuzzy Logic Photovoltaic Inverter Controller Utilizing an eZdsp F28335 Board," Energies, MDPI, vol. 9(3), pages 1-32, February.
    3. Enescu, Diana, 2017. "A review of thermal comfort models and indicators for indoor environments," Renewable and Sustainable Energy Reviews, Elsevier, vol. 79(C), pages 1353-1379.
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    5. Jin Woo Moon & Min Hee Chung & Hayub Song & Se-Young Lee, 2016. "Performance of a Predictive Model for Calculating Ascent Time to a Target Temperature," Energies, MDPI, vol. 9(12), pages 1-16, December.
    6. Santos-Herrero, J.M. & Lopez-Guede, J.M. & Flores-Abascal, I., 2021. "Modeling, simulation and control tools for nZEB: A state-of-the-art review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 142(C).
    7. Elnazeer Ali Hamid Abdalla & Perumal Nallagownden & Nursyarizal Bin Mohd Nor & Mohd Fakhizan Romlie & Sabo Miya Hassan, 2018. "An Application of a Novel Technique for Assessing the Operating Performance of Existing Cooling Systems on a University Campus," Energies, MDPI, vol. 11(4), pages 1-24, March.
    8. Halhoul Merabet, Ghezlane & Essaaidi, Mohamed & Ben Haddou, Mohamed & Qolomany, Basheer & Qadir, Junaid & Anan, Muhammad & Al-Fuqaha, Ala & Abid, Mohamed Riduan & Benhaddou, Driss, 2021. "Intelligent building control systems for thermal comfort and energy-efficiency: A systematic review of artificial intelligence-assisted techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    9. Otilia Elena Dragomir & Florin Dragomir & Veronica Stefan & Eugenia Minca, 2015. "Adaptive Neuro-Fuzzy Inference Systems as a Strategy for Predicting and Controling the Energy Produced from Renewable Sources," Energies, MDPI, vol. 8(11), pages 1-15, November.
    10. Mario Collotta & Giovanni Pau, 2015. "A Solution Based on Bluetooth Low Energy for Smart Home Energy Management," Energies, MDPI, vol. 8(10), pages 1-23, October.
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    12. Frederik Ruelens & Sandro Iacovella & Bert J. Claessens & Ronnie Belmans, 2015. "Learning Agent for a Heat-Pump Thermostat with a Set-Back Strategy Using Model-Free Reinforcement Learning," Energies, MDPI, vol. 8(8), pages 1-19, August.

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