[ANT]: A Machine Learning Approach for Building Performance Simulation: Methods and Development

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Published: Feb 7, 2019
DOI: https://doi.org/10.21625/archive.v3i1.442
Keywords:
Machine Learning ML building performance simulation Rhino3d Python Scikit-learn grasshopper

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Mahmoud M. Abdelrahman
Kafrelsheikh University - Kafrelsheikh - Egypt. The American University in Cairo - Cairo – Egypt
Ahmed Mohamed Yousef Toutou
Master Student in Alexandria University, B.Sc. Mansoura University

Abstract

In this paper, we represent an approach for combining machine learning (ML) techniques with building performance simulation by introducing four methods in which ML could be effectively involved in this field i.e. Classification, Regression, Clustering and Model selection . Rhino-3d-Grasshopper SDK was used to develop a new plugin for involving machine learning in design process using Python programming language and making use of scikit-learn module, that is, a python module which provides a general purpose high level language to nonspecialist user by integration of wide range supervised and unsupervised learning algorithms with high performance, ease of use and well documented features. ANT plugin provides a method to make use of these modules inside Rhino\Grasshopper to be handy to designers. This tool is open source and is released under BSD simplified license. This approach represents promising results regarding making use of data in automating building performance development and could be widely applied. Future studies include providing parallel computation facility using PyOpenCL module as well as computer vision integration using scikit-image.

Article Details

How to Cite
Abdelrahman, M. M., & Toutou, A. M. Y. (2019). [ANT]: A Machine Learning Approach for Building Performance Simulation: Methods and Development. The Academic Research Community Publication, 3(1), 205–213. https://doi.org/10.21625/archive.v3i1.442
Issue
Vol. 3 No. 1 (2019): Energy Efficiency and Built Environment in Developing Countries
Section
Research Papers

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