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Development of the Indian Future Weather File Generator Based on Representative Concentration Pathways

Author

Listed:
  • Naga Venkata Sai Kumar Manapragada

    (Environmental Performance and Design Lab, Faculty of Architecture and Town Planning, Technion-Israel Institute of Technology, Haifa 3200003, Israel)

  • Anoop Kumar Shukla

    (Manipal School of Architecture and Planning, Manipal Academy of Higher Education, Manipal 576104, Karnataka, India)

  • Gloria Pignatta

    (School of Built Environment, Faculty of Arts, Design and Architecture, University of New South Wales (UNSW), Sydney, NSW 2052, Australia)

  • Komali Yenneti

    (School of Architecture and Built Environment, University of Wolverhampton, Wolverhampton WV1 1LY, UK)

  • Deepika Shetty

    (Manipal School of Architecture and Planning, Manipal Academy of Higher Education, Manipal 576104, Karnataka, India)

  • Bibhu Kalyan Nayak

    (School of Architecture and Design, Manipal University Jaipur, Jaipur 303007, Rajasthan, India)

  • Venkataramana Boorla

    (Manipal School of Architecture and Planning, Manipal Academy of Higher Education, Manipal 576104, Karnataka, India)

Abstract

India’s fossil-fuel-based energy dependency is up to 68%, with the commercial and residential sectors contributing to the rise of building energy demand, energy use, and greenhouse gas emissions. Several studies have shown that the increasing building energy demand is associated with increased space-cooling ownership and building footprint. The energy demand is predicted to grow further with the conditions of global warming and the phenomenon of urban heat islands. Building designers have been using state-of-the-art transient simulation tools to evaluate energy-efficient envelopes with present-day weather files that are generated with historical weather datasets for any specific location. Designing buildings with historical climatic conditions makes the buildings vulnerable to the predicted climate change impacts. In this paper, a weather file generator was developed to generate Indian future weather files using a geo-filtering-based spatial technique, as well as the temporal downscaling and machine learning (ML)-based bias correction approach proposed by Belcher et al. The future weather files of the three representative concentration pathways of 2.6, 4.5, and 8.5 could be generated for the years 2030, 2050, 2070, 2090, and 2100. Currently, the outputs of the second-generation Canadian Earth System Model are being used to create future weather files that will aid architects, urban designers, and planners in developing a built environment that is resilient to climate change. The novelty lies in using observed historical data from present-day weather files on the typical meteorological year for testing and training ML models. The typical meteorological weather files are composed of the concatenation of the monthly weather datasets from different years, which are referred to for testing and training ML models for bias correction.

Suggested Citation

  • Naga Venkata Sai Kumar Manapragada & Anoop Kumar Shukla & Gloria Pignatta & Komali Yenneti & Deepika Shetty & Bibhu Kalyan Nayak & Venkataramana Boorla, 2022. "Development of the Indian Future Weather File Generator Based on Representative Concentration Pathways," Sustainability, MDPI, vol. 14(22), pages 1-17, November.
  • Handle: RePEc:gam:jsusta:v:14:y:2022:i:22:p:15191-:d:974325
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    References listed on IDEAS

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    1. Jentsch, Mark F. & James, Patrick A.B. & Bourikas, Leonidas & Bahaj, AbuBakr S., 2013. "Transforming existing weather data for worldwide locations to enable energy and building performance simulation under future climates," Renewable Energy, Elsevier, vol. 55(C), pages 514-524.
    2. Komali Yenneti & Riya Rahiman & Adishree Panda & Gloria Pignatta, 2019. "Smart Energy Management Policy in India—A Review," Energies, MDPI, vol. 12(17), pages 1-16, August.
    3. Rubio-Bellido, Carlos & Pérez-Fargallo, Alexis & Pulido-Arcas, Jesús A., 2016. "Optimization of annual energy demand in office buildings under the influence of climate change in Chile," Energy, Elsevier, vol. 114(C), pages 569-585.
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    6. Michele Libralato & Giovanni Murano & Alessandra De Angelis & Onorio Saro & Vincenzo Corrado, 2020. "Influence of the Meteorological Record Length on the Generation of Representative Weather Files," Energies, MDPI, vol. 13(8), pages 1-19, April.
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