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Benchmarking the building energy consumption and solar energy trade-offs of residential neighborhoods on Chongming Eco-Island, China

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  • Yang, Tianren
  • Zhang, Xiaoling

Abstract

This paper explores the effects of residential neighborhood types on building energy consumption and solar energy trade-offs with the goal of developing an operational methodology to achieve the best conditions towards a zero nonrenewable energy neighborhood. Six typical Chongming neighborhood types are identified, termed the Farmhouse, Linear-village, Nucleated-village, Townhouse, Slab and High-rise types with the effects of increased installation of solar energy panels estimated by Monte Carlo simulation. To achieve a more comprehensive view of cost-effective opportunities to improve the performance of buildings at the neighborhood scale, we use actual energy use data obtained from both the survey and electricity bills’ analysis to show that considerable variations exist due to hitherto unrecorded socioeconomic factors relating to the type of occupants involved. By concluding that the associated variables are interdependent, the paper continues with an empirical study in which the solar energy potential of each neighborhood is quantified to show that, in the case of Chongming, the Nucleated-village and Slab neighborhood types have the greatest retrofitting potential. These investigations into energy trade-offs with existing retrofitting and construction technologies therefore provide a metric for other eco-cities to use as a tool for future policy guidance.

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  • Yang, Tianren & Zhang, Xiaoling, 2016. "Benchmarking the building energy consumption and solar energy trade-offs of residential neighborhoods on Chongming Eco-Island, China," Applied Energy, Elsevier, vol. 180(C), pages 792-799.
    • Handle: RePEc:eee:appene:v:180:y:2016:i:c:p:792-799
    DOI: 10.1016/j.apenergy.2016.08.039
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    References listed on IDEAS

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    1. Burns, John Edward & Kang, Jin-Su, 2012. "Comparative economic analysis of supporting policies for residential solar PV in the United States: Solar Renewable Energy Credit (SREC) potential," Energy Policy, Elsevier, vol. 44(C), pages 217-225.
    2. Stephan, André & Stephan, Laurent, 2016. "Life cycle energy and cost analysis of embodied, operational and user-transport energy reduction measures for residential buildings," Applied Energy, Elsevier, vol. 161(C), pages 445-464.
    3. Liu, Xiaochen & Sweeney, John, 2012. "Modelling the impact of urban form on household energy demand and related CO2 emissions in the Greater Dublin Region," Energy Policy, Elsevier, vol. 46(C), pages 359-369.
    4. Milan, Christian & Bojesen, Carsten & Nielsen, Mads Pagh, 2012. "A cost optimization model for 100% renewable residential energy supply systems," Energy, Elsevier, vol. 48(1), pages 118-127.
    5. McKenna, Russell & Merkel. Erik & Fichtner, Wolf, 2016. "Energy autonomy in residential buildings: a techno-economic model-based analysis of the scale effects," Working Paper Series in Production and Energy 12, Karlsruhe Institute of Technology (KIT), Institute for Industrial Production (IIP).
    6. Zhao, Hai-xiang & Magoulès, Frédéric, 2012. "A review on the prediction of building energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(6), pages 3586-3592.
    7. Liu, Wenling & Zhang, Jinyun & Bluemling, Bettina & Mol, Arthur P.J. & Wang, Can, 2015. "Public participation in energy saving retrofitting of residential buildings in China," Applied Energy, Elsevier, vol. 147(C), pages 287-296.
    8. Rode, Philipp & Keim, Christian & Robazza, Guido & Viejo, Pablo & Schofield, James, 2014. "Cities and energy: urban morphology and residential heat-energy demand," LSE Research Online Documents on Economics 60778, London School of Economics and Political Science, LSE Library.
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    Cited by:

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    3. Yuanda Hong & Collins I. Ezeh & Wu Deng & Sung-Hugh Hong & Zhen Peng, 2019. "Building Energy Retrofit Measures in Hot-Summer–Cold-Winter Climates: A Case Study in Shanghai," Energies, MDPI, vol. 12(17), pages 1-32, September.
    4. Tianren Yang & Minghai Ye & Pei Pei & Yongjiang Shi & Haozhi Pan, 2019. "City Branding Evaluation as a Tool for Sustainable Urban Growth: A Framework and Lessons from the Yangtze River Delta Region," Sustainability, MDPI, vol. 11(16), pages 1-11, August.
    5. Chen, Yibo & Wu, Jianzhong, 2018. "Distribution patterns of energy consumed in classified public buildings through the data mining process," Applied Energy, Elsevier, vol. 226(C), pages 240-251.
    6. Jong-Won Lee & Deuk-Woo Kim & Seung-Eon Lee & Jae-Weon Jeong, 2020. "Development of a Building Occupant Survey System with 3D Spatial Information," Sustainability, MDPI, vol. 12(23), pages 1-16, November.
    7. Cai, Wei & Liu, Fei & Xie, Jun & Liu, Peiji & Tuo, Junbo, 2017. "A tool for assessing the energy demand and efficiency of machining systems: Energy benchmarking," Energy, Elsevier, vol. 138(C), pages 332-347.
    8. Cai, Wei & Liu, Fei & Zhang, Hua & Liu, Peiji & Tuo, Junbo, 2017. "Development of dynamic energy benchmark for mass production in machining systems for energy management and energy-efficiency improvement," Applied Energy, Elsevier, vol. 202(C), pages 715-725.
    9. Yang, Kun & Ding, Yan & Zhu, Neng & Yang, Fan & Wang, Qiaochu, 2018. "Multi-criteria integrated evaluation of distributed energy system for community energy planning based on improved grey incidence approach: A case study in Tianjin," Applied Energy, Elsevier, vol. 229(C), pages 352-363.
    10. Nizami, M.S.H. & Hossain, M.J. & Amin, B.M. Ruhul & Fernandez, Edstan, 2020. "A residential energy management system with bi-level optimization-based bidding strategy for day-ahead bi-directional electricity trading," Applied Energy, Elsevier, vol. 261(C).
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