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Rapid multi-objective optimization with multi-year future weather condition and decision-making support for building retrofit

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  • Shen, Pengyuan
  • Braham, William
  • Yi, Yunkyu
  • Eaton, Eric

Abstract

A method of fast multi-objective optimization and decision-making support for building retrofit planning is developed, and lifecycle cost analysis method taking into account of future climate condition is used in evaluating the retrofit performance. In order to resolve the optimization problem in a fast manner with recourse to non-dominate sorting differential evolution algorithm, the simplified hourly dynamic simulation modeling tool SimBldPy is used as the simulator for objective function evaluation. Moreover, the generated non-dominated solutions are treated and rendered by a layered scheme using agglomerative hierarchical clustering technique to make it more intuitive and sense making during the decision-making process as well as to be better presented.

Suggested Citation

  • Shen, Pengyuan & Braham, William & Yi, Yunkyu & Eaton, Eric, 2019. "Rapid multi-objective optimization with multi-year future weather condition and decision-making support for building retrofit," Energy, Elsevier, vol. 172(C), pages 892-912.
    • Handle: RePEc:eee:energy:v:172:y:2019:i:c:p:892-912
    DOI: 10.1016/j.energy.2019.01.164
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    2. Ascione, Fabrizio & Bianco, Nicola & Maria Mauro, Gerardo & Napolitano, Davide Ferdinando, 2019. "Building envelope design: Multi-objective optimization to minimize energy consumption, global cost and thermal discomfort. Application to different Italian climatic zones," Energy, Elsevier, vol. 174(C), pages 359-374.
    3. Luo, Xiaojun & Oyedele, Lukumon O., 2022. "Integrated life-cycle optimisation and supply-side management for building retrofitting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 154(C).
    4. Qu, Ke & Chen, Xiangjie & Wang, Yixin & Calautit, John & Riffat, Saffa & Cui, Xin, 2021. "Comprehensive energy, economic and thermal comfort assessments for the passive energy retrofit of historical buildings - A case study of a late nineteenth-century Victorian house renovation in the UK," Energy, Elsevier, vol. 220(C).
    5. Sheng-Yuan Wang & Kyung-Tae Lee & Ju-Hyung Kim, 2022. "Green Retrofitting Simulation for Sustainable Commercial Buildings in China Using a Proposed Multi-Agent Evolutionary Game," Sustainability, MDPI, vol. 14(13), pages 1-32, June.
    6. Pajek, Luka & Košir, Mitja, 2021. "Strategy for achieving long-term energy efficiency of European single-family buildings through passive climate adaptation," Applied Energy, Elsevier, vol. 297(C).
    7. Wang, Yuhao & Qu, Ke & Chen, Xiangjie & Zhang, Xingxing & Riffat, Saffa, 2022. "Holistic electrification vs deep energy retrofits for optimal decarbonisation pathways of UK dwellings: A case study of the 1940s’ British post-war masonry house," Energy, Elsevier, vol. 241(C).
    8. Pengying Wang & Shuo Zhang, 2022. "Retrofitting Strategies Based on Orthogonal Array Testing to Develop Nearly Zero Energy Buildings," Sustainability, MDPI, vol. 14(8), pages 1-18, April.
    9. Petkov, Ivalin & Mavromatidis, Georgios & Knoeri, Christof & Allan, James & Hoffmann, Volker H., 2022. "MANGOret: An optimization framework for the long-term investment planning of building multi-energy system and envelope retrofits," Applied Energy, Elsevier, vol. 314(C).
    10. Richarz, Jan & Henn, Sarah & Osterhage, Tanja & Müller, Dirk, 2022. "Optimal scheduling of modernization measures for typical non-residential buildings," Energy, Elsevier, vol. 238(PA).
    11. Shen, Pengyuan & Yang, Biao, 2020. "Projecting Texas energy use for residential sector under future climate and urbanization scenarios: A bottom-up method based on twenty-year regional energy use data," Energy, Elsevier, vol. 193(C).

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