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In the era of Industrial 4.0, many urgent issues in the industries can be effectively solved with artificial intelligence techniques, including machine learning. Designing an effective machine learning model for prediction and classification problems is an ongoing endeavor. Besides that, time and expertise are important factors that are needed to tailor the model to a specific issue, such as the green building housing issue. Green building is known as a potential approach to increase the efficiency of the building. To the best of our knowledge, there is still no implementation of machine learning model on GB valuation factors for building price prediction compared to conventional building development. This paper provides a report of an empirical study that model building price prediction based on green building and other common determinants. The experiments used five common machine learning algorithms namely Linear Regression, Decision Tree, Random Forest, Ridge and Lasso tested on a set of real building datasets that covered Kuala Lumpur District, Malaysia. The result showed that the Random Forest algorithm outperforms the other four algorithms on the tested dataset and the green building determinant has contributed some promising effects to the model.

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This field investigation of thermal comfort parameters in Green Building Index (GBI)-rated office buildings employing various façade-shading devices compared thermal performance in terms of four main variables: indoor air temperature, indoor relative humidity, mean radiant temperature, and indoor air velocity. Over five days of fieldwork at each building, the four variables of interest were measured, recorded, and analysed using Excel graphs. The results show that the thermal comfort performance of each building was acceptable within the parameters of the GBI Non-Residential New Construction (NRNC) Tools for Indoor Environmental Quality (IEQ). In general, observed values were good for three of the four thermal parameters: indoor air temperature, indoor relative humidity and mean radiant temperature. However, indoor air velocity fell below the acceptable range as defined by the GBI NRNC Tools. One possible reason for this negative outcome is low air exchange from the air conditioning systems in the selected buildings.

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