Tropically Adapted Passive Building: A Descriptive-Analytical Approach Using Multiple Linear Regression and Probability Models to Predict Indoor Temperature

Siti Fatihah Salleh (), Ahmad Abubakar Suleiman, Hanita Daud, Mahmod Othman, Rajalingam Sokkalingam and Karl Wagner
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Siti Fatihah Salleh: PETRONAS Research Sdn. Bhd., Off Jalan Ayer Itam, Kawasan Institusi Bangi, Kajang 43000, Malaysia
Ahmad Abubakar Suleiman: Fundamental and Applied Sciences Department, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Malaysia
Hanita Daud: Fundamental and Applied Sciences Department, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Malaysia
Mahmod Othman: Department of Information System, Universitas Islam Indragiri, Tembilahan 29212, Indonesia
Rajalingam Sokkalingam: Fundamental and Applied Sciences Department, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Malaysia
Karl Wagner: Faculty of Business Administration, Rosenheim Technical University of Applied Sciences, Hochschulstraße, 83024 Rosenheim, Germany

Sustainability, 2023, vol. 15, issue 18, 1-25

Abstract: The quest for energy efficiency in buildings has placed a demand for designing and modeling energy-efficient buildings. In this study, the thermal energy performance of a tropically adapted passive building was investigated in the warm tropical climate of Malaysia. Two mock-up buildings were built to represent a “green”, made of clay brick double-glazed passive building and a conventional, made of concrete “red” building. The mean indoor temperature of the passive building was found to be always lower than that of the red building throughout the experiment during different weather constellations. Our research builds upon existing work in the field by combining multiple linear regression models and distribution models to provide a comprehensive analysis of the factors affecting the indoor temperature of a building. The results from the fitted multiple linear regression models indicate that walls and windows are critical components that considerably influence the indoor temperature of both passive buildings and red buildings, with the exception of passive buildings during the hot season, where the roof has a greater influence than the window. Furthermore, the goodness-of-fit test results of the mean indoor temperature revealed that the Fréchet and Logistic probability models fitted the experimental data in both cold and hot seasons. It is intended that the findings of this study would help tropical countries to devise comfortable, cost-effective passive buildings that are green and energy efficient to mitigate global warming.

Keywords: passive building; green building; energy saving; thermal comfort; windows; regression analysis; probability distribution; logistic distribution; Fréchet distribution (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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