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Simulation-based optimization of an integrated daylighting and HVAC system using the design of experiments method

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  • Kim, Wonuk
  • Jeon, Yongseok
  • Kim, Yongchan

Abstract

The use of daylight in buildings to save energy while providing satisfactory environmental comfort has increased. Integration of the daylighting and thermal energy systems is necessary for environmental comfort and energy efficiency. In this study, an integrated meta-model for a daylighting, heating, ventilating, and air conditioning (IDHVAC) system was developed to predict building energy performance by artificial lighting regression models and artificial neural network (ANN) models, with a database that was generated using the EnergyPlus model. The design of experiments (DOE) method was applied to generate the database that was used to train robust ANN models without overfitting problems. The IDHVAC system was optimized using the integrated meta-model and genetic algorithm (GA), to minimize total energy consumption while satisfying both thermal and visual comfort for occupants. During three months in the winter, the GA-optimized IDHVAC model showed, on average, 13.7% energy savings against the conventional model.

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  • Kim, Wonuk & Jeon, Yongseok & Kim, Yongchan, 2016. "Simulation-based optimization of an integrated daylighting and HVAC system using the design of experiments method," Applied Energy, Elsevier, vol. 162(C), pages 666-674.
    • Handle: RePEc:eee:appene:v:162:y:2016:i:c:p:666-674
    DOI: 10.1016/j.apenergy.2015.10.153
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    7. Jing, Gang & Cai, Wenjian & Zhang, Xin & Cui, Can & Liu, Hongwu & Wang, Cheng, 2020. "An energy-saving control strategy for multi-zone demand controlled ventilation system with data-driven model and air balancing control," Energy, Elsevier, vol. 199(C).
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    15. Alibabaei, Nima & Fung, Alan S. & Raahemifar, Kaamran & Moghimi, Arash, 2017. "Effects of intelligent strategy planning models on residential HVAC system energy demand and cost during the heating and cooling seasons," Applied Energy, Elsevier, vol. 185(P1), pages 29-43.
    16. Wang, Zeyu & Liu, Jian & Zhang, Yuanxin & Yuan, Hongping & Zhang, Ruixue & Srinivasan, Ravi S., 2021. "Practical issues in implementing machine-learning models for building energy efficiency: Moving beyond obstacles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 143(C).
    17. Yang, Shiyu & Wan, Man Pun & Chen, Wanyu & Ng, Bing Feng & Dubey, Swapnil, 2020. "Model predictive control with adaptive machine-learning-based model for building energy efficiency and comfort optimization," Applied Energy, Elsevier, vol. 271(C).
    18. Salata, Ferdinando & Golasi, Iacopo & di Salvatore, Maicol & de Lieto Vollaro, Andrea, 2016. "Energy and reliability optimization of a system that combines daylighting and artificial sources. A case study carried out in academic buildings," Applied Energy, Elsevier, vol. 169(C), pages 250-266.
    19. Lei, Yunkai & Hou, Kai & Wang, Yue & Jia, Hongjie & Zhang, Pei & Mu, Yunfei & Jin, Xiaolong & Sui, Bingyan, 2018. "A new reliability assessment approach for integrated energy systems: Using hierarchical decoupling optimization framework and impact-increment based state enumeration method," Applied Energy, Elsevier, vol. 210(C), pages 1237-1250.
    20. Antonio Del Corte-Valiente & José Luis Castillo-Sequera & Ana Castillo-Martinez & José Manuel Gómez-Pulido & Jose-Maria Gutierrez-Martinez, 2017. "An Artificial Neural Network for Analyzing Overall Uniformity in Outdoor Lighting Systems," Energies, MDPI, vol. 10(2), pages 1-18, February.
    21. Baloch, Ashfaque Ahmed & Shaikh, Pervez Hameed & Shaikh, Faheemullah & Leghari, Zohaib Hussain & Mirjat, Nayyar Hussain & Uqaili, Muhammad Aslam, 2018. "Simulation tools application for artificial lighting in buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 3007-3026.
    22. Haosen Qin & Zhen Yu & Tailu Li & Xueliang Liu & Li Li, 2022. "Heating Control Strategy Based on Dynamic Programming for Building Energy Saving and Emission Reduction," IJERPH, MDPI, vol. 19(21), pages 1-27, October.
    23. Panagiotis Michailidis & Iakovos Michailidis & Socratis Gkelios & Elias Kosmatopoulos, 2024. "Artificial Neural Network Applications for Energy Management in Buildings: Current Trends and Future Directions," Energies, MDPI, vol. 17(3), pages 1-47, January.
    24. Afroz, Zakia & Shafiullah, GM & Urmee, Tania & Higgins, Gary, 2018. "Modeling techniques used in building HVAC control systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 83(C), pages 64-84.

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