Tree shadow coverage optimization on buildings in tropics by using GIS and ACO: A case study of Semnan

Document Type : Research Paper

Authors

1 PhD. Student, Department of Remote Sensing and GIS, Faculty of Natural Resources and Environment, Science and Research Branch, Islamic Azad University, Tehran, Iran

2 Assistant Professor, Department of Remote Sensing and GIS, Faculty of Natural Resources and Environment, Science and Research Branch, Islamic Azad University, Tehran, Iran

3 Assistant Professor, Faculty of Civil, Water and Environmental Engineering, Shahid Beheshti University, Tehran, Iran

4 Associate Professor, Faculty of Civil, Water and Environmental Engineering, Shahid Beheshti University, Tehran, Iran

5 Assistant Professor, Faculty of Civil Engineering Shahid Rajaiee Teacher Training University, Tehran, Iran

Abstract

Introduction: The UHI is the effect of the thermal properties of the constructions that results in higher temperatures in urban areas compared to the surrounding areas. The UHI intensifies heat waves during the summer and increases energy consumption. Well-known UHI reduction methods utilize increased vegetation such as shading surfaces through increased tree coverage on buildings. In this research, we focus on the strategic planning of shade trees, which has been shown to provide energy and long-term cost savings to enhance the environmental quality of the urban ecosystem. A simple method to create abundant shade involves planting as many trees as possible on all sides of the building. This approach, however, isn’t practical because of the cost of trees as well as water restrictions in many water-regulated communities. Similarly, excessive shading reduces the possibility of holding exposed rooftops for placing electricity-generating solar panels. The goal of this research is to consider where to optimally and precisely locate shade trees around the building such that a) the shading of the facade, windows, and doors of the building is maximized but the rooftop shade is minimized, and b) spatial optimization is creatively used to find the best tree locations quantitatively in a 3-dimensional (3D) environment.
In this study, therefore, a 3D spatial optimization model is presented to identify optimal tree locations for residential structures by integrating the GIS with ACO methods to solve this problem as a mathematical model. The modeling is done on a residential building in Semnan City, where tree shade coverage, water conservation, and solar energy potential are critical because of the hot and dry conditions.
Methodology: A 3D representation of a building is used with the height and dimensions of 3 m and 12×12 m2 respectively. The building has three windows with a dimension of 1.6 × 2 m2 on the north, east and, west facades and two windows and one door with a dimension of 2 × 3.2 m2 on the south facade. To represent a 3D tree, a simple design of pine with a height of 6 m and a crown radius of 2 m is used. Then, the theory of Duffie and Beckman (2013) is used in GIS to store the positions of 3D objects and extract shade coverage. Also, the MCLP is defined according to Church and Murray (2009). The details of the optimization steps are as follows:
a) Defining a set of possible tree positions based on the height, crown radius, surrounding area, and outlook of the building
b) Using a method for locating the first tree in all the possible positions around the building in the hot hours of certain summer days, then calculating the maximum shading on the building based on the weight of building components
c) Eliminating the possible positions within the tree crown to prevent their overlap
d) Repeating steps 2 and 3 to locate the next trees in possible positions around the building block until the trees reach the required number for shading
There are three reasons for using ACO for 3D position optimization as follows:
1) The complexity of computing the shading on the building, especially using a highly-detailed 3D model for the tree and building
2) The lack of a particular method to solve the optimization problem considering the nonlinear constraints, including trigonometric functions
3) The constant space around the building block, making it possible to locate trees anywhere Therefore, there are infinite combinations of multiple trees in the solution space.
Considering the infinite possible positions, a simplification step is required to limit the number of the available positions. So, the constant space is reduced to possible positions for locating Ni trees with two-meter spacing in the N-S and E-W directions. Further, the possible tree positions in front of the opening components are eliminated to make daylight available, have an outlook from the building, and commute through the doors. The minimum spacing of two meters between the trees and the building is set to prevent unnecessary shading on the rooftop.
Results and Discussion: A computer program is developed to maximize the shade coverage on the facade/opening and minimize it on the rooftop via ACO in Matlab. The results show that, for a building in the northern hemisphere, the trees in the north of the building have no effect in shade coverage on the building. Finding the best location for trees depends a lot on the position of the opening in the building, because of the high heat transfer through the opening versus the facade and rooftop. Table 1 shows the percentage of shade coverage on building components from 9 to 15 o’clock in four sample summer days when one to three trees are planted in suitable places around the building.
 





Table 1. Percentage of the shadow created by tress on the building components




Number of trees


The best location


Percentage of shadow on facade


Percentage of shadow on opening


Percentage of shadow on rooftop




1


Location K8 on the south


14.73%


23.08%


3%




2


Location K8 on the south & H11 on the east


26.21%


30.77%


5%




3


Location K8 on the south, H11 on the east & H3 on the west


36.04%


38.46%


7%





 
Conclusion: In this study, in addition to the determination of the shade coverage of trees, a model is used for 3D spatial optimization to predict the optimal positions of trees for shade coverage on a 3D building. For this purpose, 3D modeling and GIS location processing have been done to determine the 3D geometric characteristics of the building, aiming to optimize the tree shadow on the building. The ACO algorithm is used to create the mathematical model to determine the optimal tree position and achieve optimal tree shading on the building. This study is one of the first attempts at determining the exact position and number of trees needed for optimal tree shading on a residential building in Iran.

Keywords

References

Akbari, H., & Taha, H. (1992). The impact of trees and white surfaces on residential heating and cooling energy use in four Canadian cities. Energy, vol. 17, pp. 141–149.
Calcerano , F., & Martinelli, L. (2016). Numerical optimisation through dynamic simulation of the position of trees around a stand-alone building to reduce cooling energy consumption. Energy and Buildings, vol. 112, pp. 234–243.
Church, R., & Revelle, C. (1972). The Maximal Covering Location Problem. Papers of the Regional Science Association, vol. 6, no. 6.
Dorigo, M., & Gambardella, L. M. (1997). Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Transactions on Evolutionary Computation, vol. 1, pp. 53–66.
Duffie, J. A., & Beckman, W. A. (2013). Solar Engineering of Thermal Processes. New Jersey: John Wiley & Sons, Inc.
Ellabib, I., Calamai, P., & Basir, O. (2007). Exchange strategies for multiple Ant Colony System. Information Sciences, vol. 177, pp. 1248–1264.
Fogl, M. (2016). Influence of vegetation canopies on solar potential in urban environments. Applied Geography journal, vol. 66, pp. 73–80.
Gartland, L. (2008). Urban heat island. London: Earthscan.
Gómez-Muñoz, V. M., Porta-Gándara, M. A., & Fernández, J. L. (2010). Effect of tree shades in urban planning in hot-arid climatic regions. Landscape and Urban Planning, vol. 94, pp. 149–157.
Heisler, G. M. (1986). Eergy Savings With Trees.  Journal of Arboriculture, vol. 12, pp. 113–125.
Huang, Y. J., Akbari, H., Taha, H., & Rosenfeld, A. H. (1987). The potential of vegetation in reducing summer cooling loads in residential buildings. Journal of Climate and Applied Meteorology, vol. 26, pp. 1103–1116.
Hwang, W. H., Wiseman, P. E., & Thomas, V. A. (2015). Tree planting configuration influences shade on residential structures in four U.S. cities. Arboriculture and Urban Forestry, vol. 41, pp. 208–222.
Jadraque, E., Alegre, J., Martı, G., & Ordo, J. (2010). Analysis of the photovoltaic solar energy capacity of residential rooftops in Andalusia (Spain). Renewable and Sustainable Energy Reviews, vol. 14, pp. 2122–2130.
Kim, H. H. (1992). Urban heat island. International Journal of Remote Sensing, vol. 13, pp. 2319–2336.
Levinson, R., Akbari, H., Pomerantz, M., & Gupta, S. (2009). Solar access of residential rooftops in four California cities. Solar Energy, vol. 83, no. 12, pp. 2120–2135.
Li, Z., Zhang, Z., & Davey, K. (2015). Estimating Geographical PV Potential Using LiDAR Data for Buildings in Downtown San Francisco. Transactions in GIS, vol. 19, no. 6, pp. 930–963.
McPherson, E. G., Simpson, J. R., Peper, P. J., Maco, S. E., & Mulrean, E. (2004). Desert Southwest Community Tree Guide: ‘Benefits, Costs, and Strategic Planting. Arizona: Arizona State Land Deparment Natural Resources Division, Urban & Community Forestry Section & Arizona Community Tree Council, Inc.
Meteotological Administration, Semnan Province (2020). Meteotological Administration, Semnan Province . Retrieved February 9, 2020, from http://www.semnanweather.ir  (in Farsi).
Oke, T. R. (1982). The energetic basis of the urban heat island. Quaterly Journal of the Royal Meteorological Society, vol. 108, pp. 1–24.
Safarzadeh, H., & Bahadori, M. N. (2005). Passive cooling effects of courtyards. Building and Environment, vol. 40, pp. 89–104.
Santamouris, M. (2014). On the energy impact of urban heat island and global warming on buildings. Energy and Buildings, vol. 82, pp. 100–113.
Sawka, M., Millward, A. A., Mckay, J., & Sarkovich, M. (2013). Growing summer energy conservation through residential tree planting. Landscape and Urban Planning, vol. 113, pp. 1–9.
Shad, R., Shad, A., Mesgari, S., Aghamohammadi, H., & Molaei, D. (2009). Fuzzy topological simulation for deducing in GIS. Applied Geomatics, vol. 1, pp. 121–129.
Shaviv, E., & Yezioro, A. (1997). Analyzing mutual shading among buildings. Solar Energy, vol. 59, pp. 83–88.
Simpson J. R., & McPherson, E. G. (1996). Potential of tree shade for reducing residential energy use in California. Journal of Arboriculture, vol. 22, pp. 10–18.
Tooke, T. R., Coops, N. C., Voogt, J. A., & Meitner, M. J. (2011). Tree structure influences on rooftop-received solar radiation. Landscape and Urban Planning, vol. 102, pp. 73–81.
Wagar, J. A. (1984). Using Vegetation to Control Sunlight and Shade on Windows. Landscape journal, vol. 3, pp. 24–35.
Wentz, E. A., Rode, S., Li, X., Tellman, E. M., & Turner, B. L. (2016). Impact of Homeowner Association (HOA) landscaping guidelines on residential water use. Water Resources Research, vol. 52, pp. 3373–3386.
Zhao, Q., Wentz, E. A. & Murray, A. T. (2017). Tree shade coverage optimization in an urban residential environment. Building and Environment, vol. 115, pp. 269–280.
The Journal of Geographical Research on Desert Areas
Volume 8, Issue 2
February 2021
Pages 21-49

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