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Machine learning-based optimal design of a phase change material integrated renewable system with on-site PV, radiative cooling and hybrid ventilations—study of modelling and application in five climatic regions

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  • Zhou, Yuekuan
  • Zheng, Siqian
  • Zhang, Guoqiang

Abstract

The widespread application of advanced renewable systems with optimal design can promote the cleaner production, reduce the carbon dioxide emission and realise the renewable and sustainable development. In this study, a phase change material integrated hybrid system was demonstrated, involving with advanced energy conversions and multi-diversified energy forms, including solar-to-electricity conversion, active water-based and air-based cooling, and distributed storages. A generic optimization methodology was developed by integrating supervised machine learning and heuristic optimization algorithms. Multivariable optimizations were systematically conducted for widespread application purpose in five climatic regions in China. Results showed that, the energy performance is highly dependent on mass flow rate and inlet cooling water temperature with contribution ratios at around 90% and 7%. Furthermore, compared to Taguchi standard orthogonal array, the machine-learning based optimization can improve the annual equivalent overall output energy from 86934.36 to 90597.32 kWh (by 4.2%) in ShangHai, from 86335.35 to 92719.07 (by 7.4%) in KunMing, from 87445.1 to 91218.3 (by 4.3%) in GuangZhou, from 87278.24 to 88212.83 (by 1.1%) in HongKong, and from 87611.95 to 92376.46 (by 5.4%) in HaiKou. This study presents optimal design and operation of a renewable system in different climatic regions, which are important to realise renewable and sustainable buildings.

Suggested Citation

  • Zhou, Yuekuan & Zheng, Siqian & Zhang, Guoqiang, 2020. "Machine learning-based optimal design of a phase change material integrated renewable system with on-site PV, radiative cooling and hybrid ventilations—study of modelling and application in five clima," Energy, Elsevier, vol. 192(C).
    • Handle: RePEc:eee:energy:v:192:y:2020:i:c:s0360544219323035
    DOI: 10.1016/j.energy.2019.116608
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    References listed on IDEAS

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    5. Zhou, Yuekuan & Zheng, Siqian, 2020. "Climate adaptive optimal design of an aerogel glazing system with the integration of a heuristic teaching-learning-based algorithm in machine learning-based optimization," Renewable Energy, Elsevier, vol. 153(C), pages 375-391.
    6. Gohar Gholamibozanjani & Mohammed Farid, 2021. "A Critical Review on the Control Strategies Applied to PCM-Enhanced Buildings," Energies, MDPI, vol. 14(7), pages 1-39, March.
    7. Alimohammadian, Mehdi & Dinarvand, Saeed & Mahian, Omid, 2022. "Innovative strategy of passive sub-ambient radiative cooler through incorporation of a thermal rectifier to double-layer nanoparticle-based coating," Energy, Elsevier, vol. 247(C).
    8. Zhou, Yuekuan & Zheng, Siqian, 2020. "Machine-learning based hybrid demand-side controller for high-rise office buildings with high energy flexibilities," Applied Energy, Elsevier, vol. 262(C).
    9. Ahmad, Tanveer & Zhang, Dongdong & Huang, Chao, 2021. "Methodological framework for short-and medium-term energy, solar and wind power forecasting with stochastic-based machine learning approach to monetary and energy policy applications," Energy, Elsevier, vol. 231(C).
    10. Zhang, Guiqing & Tian, Chenlu & Li, Chengdong & Zhang, Jun Jason & Zuo, Wangda, 2020. "Accurate forecasting of building energy consumption via a novel ensembled deep learning method considering the cyclic feature," Energy, Elsevier, vol. 201(C).
    11. Qin, Di & Liu, Zhengxuan & Zhou, Yuekuan & Yan, Zhongjun & Chen, Dachuan & Zhang, Guoqiang, 2021. "Dynamic performance of a novel air-soil heat exchanger coupling with diversified energy storage components—modelling development, experimental verification, parametrical design and robust operation," Renewable Energy, Elsevier, vol. 167(C), pages 542-557.
    12. Zhou, Yuekuan & Zheng, Siqian & Liu, Zhengxuan & Wen, Tao & Ding, Zhixiong & Yan, Jun & Zhang, Guoqiang, 2020. "Passive and active phase change materials integrated building energy systems with advanced machine-learning based climate-adaptive designs, intelligent operations, uncertainty-based analysis and optim," Renewable and Sustainable Energy Reviews, Elsevier, vol. 130(C).

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