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Sensitivity Analysis and Optimization of Energy-Saving Measures for Office Building in Hot Summer and Cold Winter Regions

Author

Listed:
  • Peisong Xuanyuan

    (Department of Civil Engineering, Ningbo University, Ningbo 315211, China)

  • Yijie Zhang

    (Department of Civil Engineering, Ningbo University, Ningbo 315211, China)

  • Jian Yao

    (Department of Architecture, Ningbo University, Ningbo 315211, China)

  • Rongyue Zheng

    (Department of Civil Engineering, Ningbo University, Ningbo 315211, China)

Abstract

To effectively reduce building energy consumption and enhance indoor thermal comfort, this study developed a sensitivity analysis and multi-objective optimization method for an office building in Hangzhou considering the effects of parameter interactions. We utilized Latin hypercube sampling (LHS) to sample the parameters and subsequently simulated the energy consumption and discomfort hours for the building’s four orientations. Following this, Gaussian process regression models were established, and the feature importance ranking method (FIRM) alongside an interaction effects method were employed to quantify the sensitivity of 10 parameters. Lastly, a multi-objective optimization was performed for total energy consumption and discomfort hours, yielding a set of Pareto-optimal solutions. Through the VIKOR method, the optimal solution within the Pareto set was identified, resulting in three sets of optimal solutions.

Suggested Citation

  • Peisong Xuanyuan & Yijie Zhang & Jian Yao & Rongyue Zheng, 2024. "Sensitivity Analysis and Optimization of Energy-Saving Measures for Office Building in Hot Summer and Cold Winter Regions," Energies, MDPI, vol. 17(7), pages 1-29, April.
  • Handle: RePEc:gam:jeners:v:17:y:2024:i:7:p:1675-:d:1368409
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    References listed on IDEAS

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    1. Opricovic, Serafim & Tzeng, Gwo-Hshiung, 2007. "Extended VIKOR method in comparison with outranking methods," European Journal of Operational Research, Elsevier, vol. 178(2), pages 514-529, April.
    2. Marrel, Amandine & Iooss, Bertrand & Van Dorpe, François & Volkova, Elena, 2008. "An efficient methodology for modeling complex computer codes with Gaussian processes," Computational Statistics & Data Analysis, Elsevier, vol. 52(10), pages 4731-4744, June.
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