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Study on non-linear planning model of green building energy consumption under multi-objective optimization

Author

Listed:
  • Miao Fan

    (Zheng Zhou Railway Vocational and Technical College)

  • Danna Su

    (Zheng Zhou Railway Vocational and Technical College)

  • Mohammed Wasim Bhatt

    (Central University of Punjab)

  • Adarsh Mangal

    (Engineering College Ajmer)

Abstract

The research on economic balance and balanced competition is the initial concept that has reflected the idea of multi-objective programming which is usually encountered in the modeling of green buildings. This concept has laid the base in building a multi-objective optimization problem, for the establishment of model and calculation. By establishing a nonlinear planning model for determining the control target value of the project and by calculating the results, it is assumed that the sample is estimated as a very large likelihood, with consistency and non-biasing. In this paper several variables are analyzed and focus has been paid to weather conditions by making several interpretations: If the weather is good, the $$p_{it}$$ p it = 1, the engine can successfully complete the project amount of the plan; if constructed, when the weather is not conducive to engineering construction, then pit

Suggested Citation

  • Miao Fan & Danna Su & Mohammed Wasim Bhatt & Adarsh Mangal, 2022. "Study on non-linear planning model of green building energy consumption under multi-objective optimization," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(1), pages 437-443, March.
    • Handle: RePEc:spr:ijsaem:v:13:y:2022:i:1:d:10.1007_s13198-021-01459-3
    DOI: 10.1007/s13198-021-01459-3
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    References listed on IDEAS

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    1. Kangji Li & Lei Pan & Wenping Xue & Hui Jiang & Hanping Mao, 2017. "Multi-Objective Optimization for Energy Performance Improvement of Residential Buildings: A Comparative Study," Energies, MDPI, vol. 10(2), pages 1-23, February.
    2. Li, Hongmin & Wang, Jianzhou & Lu, Haiyan & Guo, Zhenhai, 2018. "Research and application of a combined model based on variable weight for short term wind speed forecasting," Renewable Energy, Elsevier, vol. 116(PA), pages 669-684.
    3. Lesage-Landry, Antoine & Taylor, Joshua A., 2020. "A second-order cone model of transmission planning with alternating and direct current lines," European Journal of Operational Research, Elsevier, vol. 281(1), pages 174-185.
    4. Kapil Jairath & Navdeep Singh & Vishal Jagota & Mohammad Shabaz, 2021. "Compact Ultrawide Band Metamaterial-Inspired Split Ring Resonator Structure Loaded Band Notched Antenna," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-12, May.
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