Abstract
The scaling of energy efficiency initiatives in the commercial building sector has been hampered by data limitations, information asymmetries, and benchmarking methodologies that do not adequately model patterns of energy consumption, nor provide accurate measures of relative energy performance. The reliance on simple metrics, such as Energy Use Intensity (EUI), fails to account for significant variation across occupancy, construction characteristics and other elements of a building – both its design and its users – that influence building energy consumption. Using a unique dataset of actual building energy use, physical, spatial, and occupancy characteristics – collected from New York City’s Local Law 84 energy disclosure database, the Primary Land Use Tax Lot Output (PLUTO) database, and the CoStar Group – this paper analyzes energy consumption across commercial office buildings and presents a new methodology for a market-specific benchmarking model to measure relative energy performance across peer buildings. A robust predictive model is developed to normalize across multiple building characteristics and to provide the basis for a multivariate energy performance index. The paper concludes with recommendations for data collection standards, computational approaches for building energy disclosure data, and targeted policies using k-means clustering and market segmentation.
Highlights
Criticisms of green buildings tend to revolve around the widespread marketing use of “greenwashing” – the advancement of unsubstantiated claims of environmental performance that are, at best, ambiguous and, at worst, false
The confusion around measuring energy efficiency in buildings has been exacerbated by an over-reliance on single rule-of-thumb metrics, energy use intensity (EUI), and eco-labels as proxies for energy performance
Using data from the Commercial Building Energy Consumption Survey (CBECS), this tool normalizes for multiple building characteristics and weather to predict expected energy consumption and produces a percentile rank based on a frequency distribution of energy efficiency for similar space types
Summary
Criticisms of green buildings tend to revolve around the widespread marketing use of “greenwashing” – the advancement of unsubstantiated claims of environmental performance that are, at best, ambiguous and, at worst, false. The confusion around measuring energy efficiency in buildings has been exacerbated by an over-reliance on single rule-of-thumb metrics, energy use intensity (EUI), and eco-labels as proxies for energy performance. The recent proliferation of energy disclosure policies in U.S and global cities has begun to generate significant new streams of data on patterns of energy consumption in buildings (Burr, Keicher, and Lawrence 2013). The logic behind these policies is predicated on the power of measurement and information to shift awareness and market behavior around energy consumption and generate greater demand for more energy efficient properties (Kontokosta 2013). Using data from the Commercial Building Energy Consumption Survey (CBECS), this tool normalizes for multiple building characteristics and weather to predict expected energy consumption and produces a percentile rank based on a frequency distribution of energy efficiency for similar space types
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
