January 2016

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Building Automation System Intrinsic Analytics

Before heading for third-party applications in the cloud, explore what is possible with the analytic features already built into your building automation system.
Steve Tom
Steve Tom,

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The buzzword “analytics” is often applied to stand-alone computer programs that extract data from a building automation system (BAS), analyze it, and provide suggestions on how to make the system run more efficiently.  Those systems can be very useful, but a third-party program is not always required to perform analytics and they do not always need to run in the cloud. There are many ways in which analytics are performed by the existing building automation system. Intrinsic analytics are built into the core product and do not require external programs or expensive data mapping to deliver their insights. This makes them useful to customers who may not want expensive add-on services, or whose security requirements do not permit them to export data to the cloud.

Analytics for Central Versus Distributed Control

Some sophisticated analytic functions require data from the entire system. It makes sense to implement those functions in a central computer, whether this computer is local or in the cloud. To search for new patterns, it is best to start with a large sample of trends pulled from multiple parts of the system. Looking for correlation between random streams of data is not an automatic search performed by the computer. These are performed as guided searches, using rules developed by an operator who understands how the system should operate by looking for anomalies that might indicate a problem. The rules might compare the performances of identical chillers when operating with similar loads. If one chiller consistently outperforms the other, that might indicate a problem. It would take additional searches with additional rules to determine the exact nature of the problem, but a central analytics system makes it easy to conduct these additional searches.

There is a contrasting case for analytics when you are setting up rules to detect common problems in a single piece of equipment.  These rules essentially implement logical comparisons, which a knowledgeable operator would use to detect pending failures or the need for maintenance on particular equipment or subsystems. In this case, it makes sense to implement algorithms at the level at which the data is available.  Analytics intrinsic to the BAS system have an advantage in distributed control scenarios.

Don’t overlook the fact that the user interface itself can be a powerful analysis tool.  It has often been said that “a picture is worth a thousand words,” and the user interface provides trend graphs, summary graphics and other visuals that help the user see what is happening with their system.  These can be particularly useful when combined with rule-based analytics, as the rules can tell the user that something is wrong with a particular piece of equipment and the user interface can help the user zero in on the specific problem.

Automated Logic Corporation (ALC) is constantly researching potential enhancements to its  WebCTRL® building automation system, including new analytics packages. The company is also evaluating emerging technologies such as semantic tagging. Common practices at the data model level will make it easier for stand-alone analytic programs to make sense out of the data they extract from a BAS, since mapping this data into current analytic packages can be an expensive proposition. While these enhancements will make analytics more accessible in the future, you don’t need to wait until then to begin using analytics.  Below are examples of intrinsic analytics that are available today.

Thermographic Floor Plans

For more than 20 years, ALC systems have compared the actual temperature of a room to the desired temperature (setpoint) and presented the difference as a thermographic color map. The information needed to calculate a thermographic color map is present at every zone. In fact, that calculation is already being performed at the zone-level for many control actions. It is a simple matter for each zone to communicate a single value that represents thermographic color to the BAS user interface. It would be more expensive, more complex, and contrary to the principle of distributed control for an external program to extract all the data needed to do these calculations from the zones to present a thermographic floor plan as part of a separate analytics program. The rules are known, they do not change, and the calculations are easily performed within the BAS, so it makes perfect sense to implement this as intrinsic analytics.  An example is provided as Figure 1.  It doesn’t take a sophisticated pattern recognition algorithm to see that there’s a problem with the air conditioning in the south-west corner of this building.

Figure 1: Thermographic Floor Plan

Figure 1:  Thermographic Floor Plan


Reporting has long been one of the basic functions of a building automation system. Although not typically considered an analytic tool, reports do provide a way to sort through a huge amount of data to spot anomalies. For example, sensor failures and other equipment malfunctions sometimes make it necessary to lock points until the underlying problem is repaired. Locking points puts the equipment in a manual control mode, allowing it to provide essential service. Often this comes at the price of bypassing schedules or other energy saving features and should be used only for a temporary fix. However, operators often forget to unlock the point when the need has passed. An external analytics package could analyze reams of data looking for indications that a point is locked, but it is much more efficient to simply let the BAS run a locked points report.


Trending is an extremely powerful data visualization tool. The recent addition of scatter plots to the WebCTRL system’s trending arsenal makes it even more powerful. The trends highlighted in Figure 2 show the performance of two identical chillers. Chiller 1 was clearly using more energy than chiller 2, particularly at high loads. The data pattern communicated by this graph triggered further investigations which eventually revealed a misconfigured head pressure sensor on chiller 1. Fixing this sensor reduced energy use by chiller 1 to the same level as that of chiller 2.

Figure 2: Trend Graph Comparison of Chillers

Figure 2:  Trend Graph Comparison of Chillers

Traditional trend graphs are also a very effective tool for troubleshooting problems in heating, ventilating and air conditioning (HVAC) equipment. For example, Figure 3 shows trends from a variable air volume (VAV) box with a hot water reheat valve. The top trend shows airflow through the box. The zone became occupied at 6 a.m. and shows good flow control at the flow setpoint. The next trend down shows the discharge temperature from the VAV box in yellow compared to the supply air temperature coming from the air handling unit (AHU) in blue.  From 6 a.m. to 6:45 a.m. these two temperatures were the same, as expected since the reheat valve was completely closed (third trend down). From 6:45 a.m. to 7:30 a.m., the discharge air temperature rose to about 80 °F, despite the fact that the reheat valve remained closed. It rose again between 8 a.m. and 11:30 a.m. Why did the discharge temperature rise during these two time periods? The bottom trend shows those were the time periods when the boiler system was active and hot water was pumped to all coils within the building. The control valve for this coil was leaking hot water into the coil, even though the trend graph shows the valve was supposed to be completely shut. This is an example of an equipment problem that wastes energy and makes people uncomfortable. The zone did not need heat, but the system was using energy to blow hot air into the zone anyway.

Figure 3: Trends from VAV Box with Faulty Reheat Valve

Figure 3:  Trends from VAV Box with Faulty Reheat Valve


Trend graphs are a good way to troubleshoot a problem once you know it exists, and they help guide a user into taking the right action. However, life is too short to spend hours staring at trend graphs hoping to spot problems. Hence, BAS alarms can be helpful in identifying that there is a problem. Alarm logic can reside unobtrusively in the system for years, unseen and unappreciated, until there is a problem. Then it springs into action, popping up a message on the user interface and generating a warning sound. It can generate messages on other screens as well, such as a central security monitoring station. The alarm can also send e-mails to key personnel, record data in indelible alarm logs, launch additional programs, and persist as an “acknowledged but still active” entry in the user interface.
When it comes to fault detection and diagnostics (FDD) alarms, it is recommended to confirm that the equipment in question really does have a problem than to report a problem that does not exist. The challenge is to define a set of conditions that will detect a malfunction and not generate false alarms. For example, if an air handling unit activates a direct expansion (DX) cooling stage you expect the discharge air temperature to drop. If you are programming this into alarm logic, you must be specific. For example, if you expect the temperature to drop 0.5 °F within five minutes, and the wind changes and more hot outdoor air is blown into the system, the temperature may only drop, 0.4 °F. If the FDD logic generated an alarm because the temperature did not drop as expected, it would be a false alarm that would undermine the user’s confidence. However, if the DX cooling stage actually is broken, it will fail every time the controller tries to turn it on. To avoid false alarms, the FDD logic can keep track of the failures. As an example, if it fails to start seven out of 10 times, it will signal to generate an alarm. This strategy means it takes longer to generate an alarm, but it decreases the chances of generating a false alarm.

Figure 4 illustrates the type of advanced false-alarm-avoiding FDD rule set to find the leaking hot water valve that was responsible for the trend graph shown in Figure 3.

Figure 4: Alarm Logic for Hot Water Valve

Figure 4: Alarm Logic for Hot Water Valve

[an error occurred while processing this directive] Data Storage and Handling

By definition, analytics involve the processing of data, which makes the storage and handling of data a key part of any analytics process. With its high bandwidth IP and ARC156 networks, distributed trending, and support for multiple industry standard databases, the WebCTRL system already provides a data friendly environment. Future releases of the WebCTRL system will include significant improvements in trend processing that will provide a 12x speed improvement in trend retrieval and a 120x improvement in trend storage. Whether this data is needed for a trend graph, a dashboard which summarizes data from the trend database, or an external analytics software package, the data will be there when it’s needed.

To summarize, the WebCTRL system is an analytics package. Its user-friendly graphics, extensive trend support, intrinsic FDD alarm capabilities and similar features provide the data visualization users need to guide their decision making. Existing add-on programs enhance this capability with graphic reports.  ALC continues to innovate for the future, with a focus on making data meaningful through industry leading user interfaces. The research and development team is working to bring even better intrinsic analytics to future releases of the WebCTRL system and to enhance the WebCTRL systems support for external analytics packages.  Download the full ALC whitepaper covering these topics here.

About the Author

Steve Tom, has more than 40 years of experience working with HVAC systems.  At Automated Logic he coordinated the training, documentation, and technical support programs, and frequently works with the R&D engineers on product requirements and usability.

Prior to joining Automated Logic, Steve was an officer in the U.S. Air Force where he worked on the design, construction, and operation of facilities (including HVAC systems) around the world.  He also taught graduate level courses in HVAC Design and HVAC Controls at the Air Force Institute of Technology.


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