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An Optimal Air-Conditioner On-Off Control Scheme under Extremely Hot Weather Conditions

Author

Listed:
  • Mohammed Al-Azba

    (Qatar Environment and Energy Research Institute, Hamad Bin Khalifa University, Doha 5825, Qatar
    ICube Laboratory, Université de Strasbourg-CNRS, 67000 Strasbourg, France)

  • Zhaohui Cen

    (Qatar Environment and Energy Research Institute, Hamad Bin Khalifa University, Doha 5825, Qatar)

  • Yves Remond

    (ICube Laboratory, Université de Strasbourg-CNRS, 67000 Strasbourg, France)

  • Said Ahzi

    (Qatar Environment and Energy Research Institute, Hamad Bin Khalifa University, Doha 5825, Qatar)

Abstract

Being reliant on Air Conditioning (AC) throughout the majority of the year, desert countries with extremely hot weather conditions such as Qatar are facing challenges in lowering weariness cost due to AC On-Off switching while maintaining an adequate level of comfort under a wide-range of ambient temperature variations. To address these challenges, this paper investigates an optimal On-Off control strategy to improve the AC utilization process. To overcome complexities of online optimization, a Elman Neural Networks (NN)-based estimator is proposed to estimate real values of the outdoor temperature, and make off-line optimization available. By looking up the optimum values solved from an off-line optimization scheme, the proposed control solutions can adaptively regulate the indoor temperature regardless of outdoor temperature variations. In addition, a cost function of multiple objectives, which consider both Coefficient of Performance (COP), and AC compressor weariness due to On-Off switching, is designed for the optimization target of minimum cost. Unlike conventional On-Off control methodologies, the proposed On-Off control technique can respond adaptively to match large-range (up to 20 ∘ C) ambient temperature variations while overcoming the drawbacks of long-time online optimization due to heavy computational load. Finally, the Elman NN based outdoor temperature estimator is validated with an acceptable accuracy and various validations for AC control optimization under Qatar’s real outdoor temperature conditions, which include three hot seasons, are conducted and analyzed. The results demonstrate the effectiveness and robustness of the proposed optimal On-Off control solution.

Suggested Citation

  • Mohammed Al-Azba & Zhaohui Cen & Yves Remond & Said Ahzi, 2020. "An Optimal Air-Conditioner On-Off Control Scheme under Extremely Hot Weather Conditions," Energies, MDPI, vol. 13(5), pages 1-21, February.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:5:p:1021-:d:324951
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    References listed on IDEAS

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    1. Sim, Lik Fang, 2014. "Numerical modelling of a solar thermal cooling system under arid weather conditions," Renewable Energy, Elsevier, vol. 67(C), pages 186-191.
    2. Khaled Shaaban & Deepti Muley & Dina Elnashar, 2018. "Evaluating the effect of seasonal variations on walking behaviour in a hot weather country using logistic regression," International Journal of Urban Sciences, Taylor & Francis Journals, vol. 22(3), pages 382-391, July.
    3. Touati, Farid & Al-Hitmi, M.A. & Chowdhury, Noor Alam & Hamad, Jehan Abu & San Pedro Gonzales, Antonio J.R., 2016. "Investigation of solar PV performance under Doha weather using a customized measurement and monitoring system," Renewable Energy, Elsevier, vol. 89(C), pages 564-577.
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    Cited by:

    1. Yassine Chemingui & Adel Gastli & Omar Ellabban, 2020. "Reinforcement Learning-Based School Energy Management System," Energies, MDPI, vol. 13(23), pages 1-21, December.
    2. Yudong Xia & Shu Jiangzhou & Xuejun Zhang & Zhao Zhang, 2020. "Steady-State Performance Prediction for a Variable Speed Direct Expansion Air Conditioning System Using a White-Box Based Modeling Approach," Energies, MDPI, vol. 13(18), pages 1-17, September.
    3. Ferenc Szodrai, 2020. "Heat Sink Shape and Topology Optimization with Pareto-Vector Length Optimization for Air Cooling," Energies, MDPI, vol. 13(7), pages 1-15, April.

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