Light & Engineering 30 (3)

Volume 30
Date of publication 06/06/2022
Pages 27–33

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Numerical Modelling of Urban Built Form Geometry for Maximum Daylight Potential L&E, Vol.30, No.3, 2022

Articles authors:

Shambhabi Chatterjee, Kamalika Ghosh

Shambhabi Chatterjee, M.Tech. in Illumination Technology and Design from Jadavpur University, Kolkata, India. She is working in a reputed lighting company as the Senior Design Engineer. In addition, she is pursuing research work in this field

Kamalika Ghosh, Ph.D. She has 20 years industrial experiences. She was Director of Schoolof Illumination, Science, Engineering and Design, Jadavpur University, Kolkata, India. She has more than 60 number of published papers. Guided 60 nos. M.E. / M.Tech Thesis and Sole supervisor of two nos. awarded Ph.D. She is a Life Fellow of Institution of Engineers, India, Indian Society of Lighting Engineers and Association of Engineers, India

Abstract:

The main focus of this research is urban development and planning to use maximum solar irradiance for Kolkata region. To develop this building management system, the impact of different urban built form geometry types and its orientation on the daylighting potential has been experimentally observed. The approach and methodology of this study is to highlight that there is uneven distribution of illumination in the interiors of various built forms with different orientations, caused due to overcrowding and shadowing of neighbouring buildings, and to suggest the optimum or desirable layout of built form in an urban fabric. The study is also significant as the city Kolkata falls under tropical climatic condition where the solar irradiance is abundant. The average illuminance has been obtained in different built forms with different orientations on hourly basis from morning to evening for different typologies of urban built form geometries. The results have then been analysed and the findings from the study suggests the appropriate built form typologies and their orientations to get the optimum or desirable illuminance level inside the buildings and hence saving power consumption and thus offering better opportunities in reducing electrical load in those built forms and thus contributing to the future solution for energy crisis.

References:

1. m.economictimes.com- February 2,2020.
2. www.indiaenergyportal.org
3. Quartulli, M., Datcu, M. Stochastic geometrical modelling for built-up area understanding from a single SAR intensity image with meter resolution // IEEE Transactions on Geoscience and Remote Sensing, 2004, Vol. 42, #9, pp. 1996–2003.
4. Sommer, C., Eckhoff, D., German, R., Dressler, F. A computationally inexpensive empirical model of IEEE802.11p radio shadowing in urban environments // 2011 Eighth International Conference on Wireless On- Demand Network Systems and Services, Bardonecchia, 2011, pp. 84–90.
5. Zhoming, Sh., Jimeno, Fonseca A., Schlueter, A. A review of simulation – based urban form generation and optimization for energy driven urban design // Elsevier Buildings and Environment, 2017, Vol. 121, pp. 119–129.
6. www.thecoloursof India> Interesting Facts> Geography
7. www.indiaenergyportal.org
8. wwwtimeanddate.com/sun/india/newdelhi
9. Cleveland, C., Costanza, R., Hall, C., Kaufmann, R. Energy and the U.S. economy: A biophysical perspective // Science, 1984, Vol. 225, pp. 890–897.
10. Anado, M., Poggi, F. Towards Solar Urban Planning: A new step for Better Energy Performance // Energy Procedia, 2012, Vol. 30, pp. 1261–1273.
11. Gadsden, S., Rylatta, M., Lomas, K., Robinson, D. Predicting the urban solar fraction: a methodology for energy advisers and planners based on GIS // Energy and Buildings, 2003, Vol. 35, pp. 37–48.
12. Theodoridou, I., et al. Assessment of retrofitting measures and solar systems’ potential in urban areas using Geographical Information Systems: Application to a Mediterranean city // Renewable and sustainable energy reviews, 2012, Vol. 16, pp. 6239–6261.
13. Diaz-Vilarino, L., Laguela, S., Armesto, J., Arias, P. Semantic as-built 3d models including shades for the evaluation of solar influence on buildings // Solar Energy, 2013, Vol. 92, pp. 269–279.
14. Izquierdo, S., Rodrigues, M., Fueyo, N. A Method for Estimating the Geographical Distribution of the Available Roof Surface Area for Largescale Photovoltaic Energy-potential Evaluations // Solar Energy, 2008, Vol. 82, pp. 929–939.
15. Brito, C., Gomes, N., Santos, T., Tenedorio, J.A. Photovoltaic potential in a Lisbon suburb using Li-DAR data // Solar Energy, 2012, Vol. 86, pp. 283–288.
16. Redweik, P., Catita, C., Brito, M. Solar energy potential on roofs and facades in an urban landscape // Solar Energy, 2013, Vol. 9, pp. 332–341.
17. Zhang, J., Xu, L., Shabunko, V., Tay, S., En R., Sun, H., Lau, S., Siu Y., Reindl, T. Impact of urban block typology on building solar potential and energy use efficiency in tropical high-density city // Applied Energy, Elsevier, 2019, Vol. 240 (C), pp. 513–533.
18. Amado, M., Poggi F. Solar urban planning: a parametric approach // International Conference on Solar Heating and Cooling for Buildings and Industry September 23–25, 2013, Freiburg, Germany.
19. Hensen, J. L. M., Lamberts, R. Building Performance Simulation for Design & Operation.
20. Rui, Yi. Urban Growth Modelling based on Land Use changes and Road Network Expansion, KTH, Architecture and the Built Environment.

Keywords

DOI: https://doi.org/10.33383/2021-014

Article in RUS:
Svetotekhnika # 1, 2022, pp. 33–38

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