Machine learning model for green building design prediction

Mustika Sari, Mohammed Ali Berawi, Teuku Yuri Zagloel, Rizka Wulan Triadji


Green Building (GB) is a design concept that implements sustainable processes and green technologies in the building’s life cycle. However, the design process of GB tends to take longer than conventional buildings due to the integration of various green requirements and performances into the building design. Technological advances are continually improving the quality of human life by providing solutions to problems they encounter, such as the machine learning (ML) technique utilized to develop predictive and classification models. This study aims to develop a GB design prediction by employing an ML approach by considering four GB design criteria: energy efficiency, indoor environmental quality, water efficiency, and site planning. A dataset of GB projects collected from a private construction company based in Jakarta was used to train and test the ML model. Mean Square Error (MSE) was used to evaluate the model accuracy. The comparison of MSE results of the conducted experiments showed that the combination of the ANN method with the IF-ELSE algorithm resulted in the most accurate ML model for GB design prediction with an MSE of 1.3, creating a predictive model that improves the time efficiency of GB design process.


machine learning; green building; design prediction; artificial neural network



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