by Jim Newman of Linnean Solutions
Data quality may be the elephant in the room when we talk about widespread energy and water use reporting. Energy and water use reporting in the US has grown in importance because of a combination of corporate reporting needs and municipal regulation. This article looks at two key types of data quality problems that can arise when reporting energy or water use and suggests some ways to manage them, focusing mostly on energy use.
Data Accuracy – Does the Information Correctly Reflect Reality?
Most building engineers feel as though they have a good handle on the problem of data accuracy. If they can get the right meters at the right spots in a building, then they have a pretty good idea that the data from those meters will reflect the actual energy use of the building at the right level of detail. Unfortunately, the realities of building ownership and management often make this level of information accuracy impossible to achieve. For example, there may only be one meter for 4 buildings, but one of those buildings also has 3 other meters that track tenant energy use.
Such a metering scenario is a classic example of a data accuracy problem. The data does not accurately describe energy use for a building, so any energy analysis needs to include some modeling or other system of educated guessing. Engineers feel as though they understand this set of issues and have tools to manage them, even if the output of an analysis does not explicitly cite the methods that are used. Unfortunately, this sort of data accuracy problem presents trouble in a municipal reporting setting, where the analysis is done completely separate from the knowledge about specific buildings.
Data Matching – Does Your Energy Data Match the Municipal Definition of Your Building?
The problem of data matching is not well understood among building energy professionals. An obvious example of this problem occurs when a municipality requires energy use reporting and uses the property tax rolls to match buildings to energy data. The data sets do not match up. Some buildings have multiple property tax records, and some have no record at all. Many data sets do match, but it is very hard to tell which match and which do not match.
Another example of the data matching problem comes from collecting building energy use from a building’s energy management system. In many cases, energy management systems do not look at outdoor energy uses such as lighting, nor may they look at process loads such as server rooms. The energy system’s view of your building does not match the utility’s view of your building (not to mention the building manager’s view). A similar data matching problem comes from using the EPA’s Portfolio Manager tool for municipal energy reporting. The problem here is that the data collected in Portfolio Manager does not match the public’s understanding of a building, because of missing data. New York City has been struggling with reports that show older buildings performing better than newer, high performance buildings because the data does not include such things as occupant density and server room loads. The public sees seemingly equivalent information about buildings that neglects key distinguishing data.
Energy Use in Buildings
It used to be that a sophisticated building had pneumatic controls managing the heating and cooling systems and banks of switches for the lights. If you wanted to know the overall energy performance of your building, you looked at your utility bills. This system had inherent limitations; in the timeliness of the data – months old, and the accuracy of the data – bills were often estimated. Modern building control systems handle complicated equipment as well as more complicated tasks, including the provision of energy reporting and performance diagnostics, but some of the reporting limitations are the same.
Tracking the overall use of energy or water in a building is typically done with meters of various sorts. At the most basic level, the meters are read by the utility and that information is available to building operators in the form of a bill. The more sophisticated energy monitoring systems in larger, newer buildings use meters that report data more frequently, either directly to the utility or directly to building operators, or both. The basic components of a monitoring system are meters, data logging and storage, analysis tools, and display or reporting.
In Building Performance Monitoring Systems, large amounts of data move in one direction, from meters to presentation and reporting.
The costs associated with installing and managing performance monitoring systems have been dropping over the last several years. Because of the simplicity of the systems and because of the low costs, monitoring systems have gained in popularity with building managers. Well designed monitoring systems solve many of the hardest data accuracy problems.
Strategies to Maintain Data Quality
Building owners and managers can avoid data accuracy problems by carefully mapping exactly what information is collected, and checking the information sources against each other. A classic problem for smaller building owners trying to track their energy use is that utility bills are notoriously inaccurate. Utility information can be checked against prior years and against degree days for the time period. These are among the tools for maintaining appropriate data accuracy.
Data matching problems are more insidious. They are harder to see, harder to catch, and harder to fix. The first tool in a manager’s toolbox is just realizing that non-matching data sets are a problem. If you realize that your utility may not understand that some of the power from a specific meter actually goes to another building, you can act to correct that information. A second important tool is more of a data modeling exercise, in which the building manager maps what is in the data sets that are supposed to match. For example, the building manager might look at the property tax record of a building to match the information in that record with the information in a utility bill. While this process might sound complicated, it is straight forward, once begun, and important for clearing up data matching problems.