Predicting Green Building
Performance Over Time:
Data Mining Untapped Information In LEED
Author(s)
A. Jeerage, B. Erwine, S. Mallory & V. Agarwal
Abstract
This paper will present a method for data mining building performance
information available in the LEEDTM rating system project applications to show
predicted environmental impact and improved performance over time. A study
initiated by the City of Seattle analyzed data from over 50 LEED projects within
the city limits to quantify the climate change impact of LEED buildings,
developing aggregate performance predictions for water, energy and solid waste
measures and savings. This paper demonstrates the efficacy of mining LEED
derived performance data to make predictions about environmental impact,
including carbon reduction, of green building programs using the City of Seattle
Performance Evaluation Program as an example. This paper probes key findings
regarding the most common green building measures, aggregated savings, the
market penetration of specific strategies, and opportunities lost. The LEED
Documentation Data Mining Methodology® magnifies its value if used to
compare varying building cohorts to identify regional, programmatic, and ownerrelated
performance patterns and to inform continuous improvement of
sustainable building approaches. The paper explores how this data mining
methodology could be adapted and developed for any rating system and any
cohort of buildings, and thus utilized to predict sustainable design and related
climate impacts of building programs.
Keywords: data-mining, sustainability, LEED.
Keywords
data-mining, sustainability, LEED
Related Book
Other papers in this volume
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