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Building Performance Database

Building Performance Database

Overview

The Building Performance Database (BPD) unlocks the power of building data by providing access to the largest publicly-available dataset of information about the physical and operational characteristics of real buildings. The BPD enables users to perform statistical analysis on an anonymous dataset of hundreds of thousands of commercial and residential buildings from across the country. Users can examine specific building types and geographic areas, compare performance trends among similar buildings, identify and prioritize cost-saving energy efficiency improvements, and assess the range of likely savings from these improvements.

The BPD is temporarily down as we work to enhance and improve the platform. It will be back and better than ever in January of 2015, with significant enhancements in analytical capability, user experience, API functionality and new data sources.  If you have an existing account, you will receive an email when the new version is launched. If you do not have an account but would like to be notified when the BPD returns, please subscribe for additional updates. If you have other questions, please contact us below.

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Key Features
  • The BPD contains actual data on hundreds of thousands of existing buildings -- not modeled data or anecdotal evidence.
  • The BPD includes a variety of tools that make it easier to analyze and compare data from different kinds of buildings.
  • The BPD enables statistical analysis without revealing information about individual buildings.
  • The BPD cleanses and validates data from many sources and translates it into a standard format.
  • A robust API allows external software to utilize BPD data.
Analysis Tools
  • Peer Group Tool. Allows users to peruse the BPD and create peer groups based on specific building types, locations, sizes, ages, equipment and operational characteristics. Users can compare the energy use of their own building to a peer group of BPD buildings via three different analysis views:
    • Bar Chart. The bar chart shows the number of buildings in the peer group that fall within consistent intervals of energy use intensity (EUI) i.e. the annual energy use per gross square foot of the building.
    • Scatter Plot. The scatter plot allows users to compare the EUI against various building characteristics. A more linear trend means that the building characteristic is more correlated with EUI, while a wider scatter means weaker correlation
    • Table. The table allows users to generate and export characteristics of the peer group categorized by state or facility type. The table shows the building count, median EUI (kBtu/ft2), median floor area, median year built, and median hours occupied.
  • Performance Comparison Tool.  Allows users to compare any two peer groups of buildings. For example, users can compare the energy performance of large office buildings in different cities. The Performance Comparison Tool can also be used estimate the impact of changing the technology used for a particular building system in a specific kind of building (e.g., cooling equipment in big box retail stores). This is done by computing the the difference in energy use between similar buildings that use one technology versus another. Users can select whether this analysis is done based on multivariate regression or actuarial methods. The result is a distribution showing the likelihood of achieving different levels of energy savings, presented as a percent reduction in total energy use.
  • Application Programming Interface (API). Allows external software to directly connect to the BPD to conduct analysis and pull the results into other tools.
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Contribute Data

The value of the BPD depends upon the amount and quality of data it contains. Basic building information and monthly energy consumption data can be used to compare similar buildings and identify high and low performers. Richer data, such as equipment information and interval meter data, will enable statistical analysis of potential retrofit measures and other advanced analysis. If you are interested in contributing data, please contact us.