Green building valuation based on machine learning algorithms / Thuraiya Mohd ... [et al.]

Mohd, Thuraiya and Jamil, Syafiqah and Masrom, Suraya and Ab Rahim, Norbaya (2021) Green building valuation based on machine learning algorithms / Thuraiya Mohd ... [et al.]. In: Virtual Go-Green: Conference and Publication (V-GoGreen 2020), 29-30 September 2020, Universiti Teknologi MARA, Cawangan Perak Kampus Seri Iskandar.

Abstract

In the cycle of Industrial Revolution 4.0 (IR 4.0), many issues in the industries can be solved with implementation of artificial intelligence approaches, including machine learning models. Designing an effective machine learning model for prediction and classification problems is a continuous effort. In addition, time and expertise are important factors needed to adapt the model to a specific problem such as green building housing development. Green building is known as a potential method to improve building performance efficiency. To our knowledge, there is still no implementation of machine learning models on green building valuation features for building price prediction compared to conventional building development. This paper provides an empirical study report, that building price predictions are based on green building and other general determinants. This experiment used five common machine learning algorithms namely 1) Linear Regressor, 2) Decision Tree Regressor, 3) Random Forest Regressor, 4) Ridge Regressor and 5) Lasso Regressor tested on a real estate data-set of covering Kuala Lumpur District, Malaysia. 3 set of experiments was conducted based on the different feature selections and purposes The results show that the implementation of 16 variables based on Experiment 2 has given a promising effect on the model compare the other experiment, and the Random Forest Regressor by using the Split approach for training and validating data-set outperformed other algorithms compared to Cross-Validation approach. The research will provide an appropriate model in predicting the price of a green building which is beneficial to the government agencies and industry practices

Metadata

Item Type: Conference or Workshop Item (Paper)
Creators:
Creators
Email / ID Num.
Mohd, Thuraiya
UNSPECIFIED
Jamil, Syafiqah
UNSPECIFIED
Masrom, Suraya
UNSPECIFIED
Ab Rahim, Norbaya
UNSPECIFIED
Subjects: N Fine Arts > NA Architecture
N Fine Arts > NA Architecture > Sustainable architecture
Divisions: Universiti Teknologi MARA, Perak > Seri Iskandar Campus > Faculty of Architecture, Planning and Surveying
Journal or Publication Title: Virtual Go-Green: Conference and Publication (V-GoGreen 2020)
Event Title: Virtual Go-Green: Conference and Publication (V-GoGreen 2020)
Event Dates: 29-30 September 2020
Page Range: pp. 292-299
Keywords: machine learning model; algorithm; green building; property features
Date: September 2021
URI: https://ir.uitm.edu.my/id/eprint/74721
Edit Item
Edit Item

Download

[thumbnail of 74721.pdf] Text
74721.pdf

Download (1MB)

ID Number

74721

Indexing

Statistic

Statistic details