Selection of target LEED credits based on project information and climatic factors using data mining techniques

Abstract

Developed by the United States Green Building Council, Leadership in Energy and Environmental Design (LEED) is a credit-based rating system that provides third-party verification for green buildings. Selection of target credits is important yet challenging for LEED managers because various factors such as target certification grade level and building features need to be considered on a case-by-case basis. Local climatic factors could affect the selection of green building technologies and hence the target credits, but currently there is no research suggesting target LEED credits based on climatic factors. This paper presents a methodology for the selection of target LEED credits based on project information and climatic factors. This study focuses on projects certified with LEED for Existing Buildings (LEED-EB). Information of 912 projects and their surrounding climatic circumstances was collected and studied. 55 classification models for 47 LEED-EB credits were then constructed and optimized using three classification algorithms - Random Forests, AdaBoost Decision Tree, and Support Vector Machine (SVM). The results showed that Random Forests performed the best in most of the 55 classification models. With a combination of the three algorithms, the trained classification models were used to develop a web-based decision support system for LEED credit selection. The system was tested using 20 recently certified LEED projects, and the results showed that our system had an accuracy of 82.56%.