The Buildings Performance Database offers several analysis tools for exploring building data and forecasting financial and energy savings, including a Peer Group Tool, a Performance Comparison Tool, and a Financial Forecasting Tool under development.
Peer Group Tool
The Peer Group Tool allows users to browse the BPD, define peer groups, and analyze performance.
- Users can create Peer Groups by filtering the dataset based on parameters such as building type, location, floor area, age, occupancy, and system characteristics such as lighting and HVAC type.
- The bar chart, scatter plot, and table show the energy performance distribution of those buildings in terms of:
- Energy metrics such as energy use intensity based on source consumption, site consumption, electricity consumption, or fuel consumption.
- Building characteristics such as gross floor area, year built, and hours occupied
- Median building characteristics such as median EUI, median floor area, median year built, and median hours occupied, based on state or facility type.
- More variables will become available for analysis as the dataset grows.
- Users can also enter information about their own building to see how it compares to the peer group.
PERFORMANCE COMPARISON TOOL
The Performance Comparison Tool allows users to analyze the energy performance impact of specific technology changes for a peer group of buildings. By selecting two technologies from drop down lists, the user can compare buildings that utilize one technology against peer buildings that utilize another.
- Allows users to analyze the savings potential of specific technology changes by comparing energy use for a subset of buildings within the peer group that utilize one technology against those that utilize another, using a one-to-one comparison between each of the buildings in the two sub-groups.
- Results are presented as a bar chart showing the likelihood of achieving different levels of energy savings, or the performance risk, of the retrofit measure, calculated based on the percentage of one-to-one comparisons that resulted in each level of energy savings. The horizontal axis shows the percent change in energy use, while the vertical axis shows the percentage of the one-to-one comparisons that resulted in that level of energy savings. In other words, the y-axis is equivalent to the probability of observing the percent change in energy use specified on the x-axis.
- The user can move the green bubble showing estimated savings to see the probability of achieving different levels of savings.
- Note that this analysis does not take into account differences between buildings such as characteristics outside of the parameters being used as controls for the peer group and the technologies being compared in the retrofit analysis.
Example: Below is an example retrofit in which we analyze the change in EUI for residential buildings in California that use electric heating, versus houses with heat pumps. The green circle corresponds to the user-selected energy savings goal, in this example 10%. The height of the histogram at 10% on the x-axis represents the probability that houses in the peer group with heat pumps have exactly 10% lower EUI than houses with electric furnaces. The probability that buildings will reduce EUI at least 10% is given by the area of the histogram to the left of 10% (green bars). The retrofit analysis tool computes this area and presents the result in the top right corner, in this case 45%. Increasing the EUI reduction target to 25% reduces the probability of achieving that target to 38% (the circle can be moved left or right and the percentage probability that the EUI will decrease/increase will change accordingly).
Retrofit analysis for residential buildings in California with electric heating. Candidate retrofit is to replace furnaces with heat pumps. The analysis shows a 69% probability of reducing EUI by 10% or more.
FEATURES UNDER DEVELOPMENT
Weather Normalization Toggle
- Uses a statistical model to compute the energy use impact of outdoor air temperature for each building, and to predict energy use given a standard set of temperature conditions.
- By applying the same temperatures to every building, we can remove the energy use impacts of seasonally or geographically specific weather conditions, such as an unusually hot summer.
- Analyzing weather normalized energy use results in a fairer comparison of buildings with energy data collected in different years, or of buildings located in different climates, allowing users to select peer groups of buildings that span different climate zones and measurement years without seeing the energy use impacts of severe or atypical weather.
- Weather normalization will be offered as a toggle so users can opt to view or analyze either weather normalized or raw energy data.