Analysis of the Related Credits in LEED Green Building Rating System Using Data Mining Techniques

Abstract

Like many green building rating systems, LEED (Leadership in Energy and Environmental Design) certifies green buildings as different grades according to the number of credit points the buildings have achieved. LEED project consultants often attempt to maximize the number of credit points to be achieved strategically with limited budgets and resources. For example, some green building technologies and features can be used to achieve multiple credits with little additional effort. Therefore, some applicants have studied the relationship and similarity in scope among the credits in green building rating systems, thereby finding ways to achieve multiple related credits. Some green building guides, such as the official LEED reference guide, also give suggestions on related credits. However, there has been a lack of study testing the strength of these suggestions. This paper aims to evaluate the strength of these suggested relationships by using data mining techniques. A database of certified green building projects was constructed based on the data from the USGBC website. The credits achieved by these projects were analyzed using association rule mining techniques. The strength of the suggested credit rules were identified according to the calculated outcomes. The results show that some suggested credit rules are related and commonly co-occur.