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Multi-Objective Optimization-Driven Research on Rural Residential Building Design in Inner Mongolia Region

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  • Dezhi Zou

    (School of Architecture and Design, Harbin Institute of Technology, Harbin 150001, China
    Key Laboratory of Cold Region Urban and Rural Human Settlement Environment Science and Technology, Ministry of Industry and Information Technology, Harbin 150001, China
    School of Architecture, Inner Mongolia University of Technology, Hohhot 010051, China
    Key Laboratory of Green Building at Universities of Inner Mongolia Autonomous Region, Hohhot 010051, China)

  • Cheng Sun

    (School of Architecture and Design, Harbin Institute of Technology, Harbin 150001, China
    Key Laboratory of Cold Region Urban and Rural Human Settlement Environment Science and Technology, Ministry of Industry and Information Technology, Harbin 150001, China)

  • Denghui Gao

    (School of Architecture, Inner Mongolia University of Technology, Hohhot 010051, China
    Key Laboratory of Green Building at Universities of Inner Mongolia Autonomous Region, Hohhot 010051, China)

Abstract

According to the China Building Energy Consumption and Carbon Emissions Research Report (2023), the construction industry accounts for 36.3% of total societal energy consumption, with residential buildings contributing significantly due to their extensive coverage and high operational frequency. Addressing energy efficiency and carbon reduction in this sector is critical for achieving national sustainability goals. This study proposes an optimization methodology for rural dwellings in Inner Mongolia, focusing on reducing energy demand while enhancing indoor thermal comfort and daylight performance. A parametric model was developed using Grasshopper, with energy consumption, thermal comfort (PPD), and Useful Daylight Illuminance (UDI) simulated through Ladybug and Honeybee tools. Key parameters analyzed include building morphology, envelope structures, and indoor thermal environments, followed by systematic optimization of building components. To refine multi-objective inputs, a specialized wall database was established, enabling categorization and dynamic visualization of material properties and construction methods. Comparative analysis demonstrated a 22.56% reduction in energy consumption, 19.26% decrease in occupant thermal dissatisfaction (PPD), and 25.44% improvement in UDI values post-optimization. The proposed framework provides a scientifically validated approach for improving energy efficiency and environmental adaptability in cold-climate rural architecture.

Suggested Citation

  • Dezhi Zou & Cheng Sun & Denghui Gao, 2025. "Multi-Objective Optimization-Driven Research on Rural Residential Building Design in Inner Mongolia Region," Energies, MDPI, vol. 18(7), pages 1-29, April.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:7:p:1867-:d:1629656
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    References listed on IDEAS

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    1. Ascione, Fabrizio & Bianco, Nicola & Mauro, Gerardo Maria & Vanoli, Giuseppe Peter, 2019. "A new comprehensive framework for the multi-objective optimization of building energy design: Harlequin," Applied Energy, Elsevier, vol. 241(C), pages 331-361.
    2. Zhikun Ding & Jinze Li & Zhan Wang & Zhaoyang Xiong, 2024. "Multi-Objective Optimization of Building Envelope Retrofits Considering Future Climate Scenarios: An Integrated Approach Using Machine Learning and Climate Models," Sustainability, MDPI, vol. 16(18), pages 1-19, September.
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