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Analysis Study Research Paper | Architecture & Planning | Kuwait | Volume 11 Issue 12, December 2022 | Popularity: 5 / 10

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A Statistical Approach to Determine the Relationship between the Shape of Skyscrapers and Energy Consumption in Kuwait City

Yousef Y. Al-Hashash, Mohammad Alissan AlGhamdi

Abstract: The skyscrapers in Kuwait draw the new skyline of Kuwait City. Wandering through streets of Kuwait City, one can observe new skyscrapers already formed the new urban image of modern Kuwait City, each skyscraper with a unique shape and form, width and height, glass, and solid facade. One, therefore, questions the energy performance of new skyscrapers and electrical consumption load rests on the city. Sixteen skyscrapers are selected as case studies from four areas in Kuwait City: Jebla, Sharq, Mirqab and Bneid Al-Qar. On the other hand, eight shape variables are determined including: X1 = Area of Average Floor Plan in (m2), X2 = Height (m), X3 = Volume in (m3), X4 = Surface Area (m2), X5 = Compactness (m), X6 = Shape Coefficient in (m-1), X7 = Relative Compactness and X8 = South Exposure (m-1). The purpose of the study is to study the relationship between the eight variables and the electrical consumption of each of the 16 case studies. A multiple linear regression operation (MLR) within the Microsoft Excel add-ins had been performed, then, a mathematical hypothesis had been employed after the statistical process to discover the overall model strength by (F-Significance) where its value was between 0 and 0.05 which is statistically significant and through the (P-Values). Four variables had been succeeded with values between 0 and 0.05, and they are X1 (Area of Average plan), X2 (Height), X5 (Compactness), and X7 (Relative compactness). Consideration, therefore, must be given to the four mentioned variables from the initial design stage to achieve efficient and sustainable skyscrapers at Kuwait City and other cites of Arabian Gulf.

Keywords: Skyscrapers, Tall Buildings, electrical consumption, Energy Consumption, Statistical Regression

Edition: Volume 11 Issue 12, December 2022

Pages: 1262 - 1269

DOI: https://www.doi.org/10.21275/SR221228033003

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