April 2011
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Finding the Needle
The emerging field of analytics is the key to turning the data from smart systems into actionable intelligence.
John Petze

John Petze, C.E.M.
Partner
SkyFoundry

We finally got our wish! Modern control systems and smart devices give us access to tremendous amounts of data – environmental conditions, energy use, equipment operation and many other facets of building operations. In this data are the keys to better building performance – trends, correlations, exceptions, deviations, control loop inefficiencies, and equipment faults – but how do we find them?

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The emerging field of analytics is the key to turning the data from smart systems into actionable intelligence. Analytic tools – software that has the capability to automatically analyze building, energy and equipment data to find patterns of interest – can uncover control issues and equipment faults showing us the path to better facility operation.

Data MeaningSemantics
In order to begin the analytics process we have to know the meaning of our data. For example, if we get a data item from a BAS and it has a value of 77.6 we can’t do any effective analysis until we understand whether it is 77.6 degrees F, or PSI, or RPM, or kW, etc. “Units” is one good example of meaning that we need to begin the analytics process, but it is by no means the only one.

Continuing with our example, perhaps the point with the value of 77.6 is named zn3-wwfl4. If I am familiar with the system and the naming conventions used when it was installed I may be able to determine that means Zone 3, West Wing, Floor 4.  Now I have a bit of information to work with. If I know the building well I may also be able to tell that zn3-wwfl4:

Is a zone temperature
Is an exterior zone
Is south facing
Is supplied by a VAV box
Is served by AHU-1
Is operated on occupancy schedule #1 which is 7:30 AM - 6:30 PM
Has an occupied cooling setpoint of 74 degrees F

This “data about data” is called meta-data. This meta-data enables me to understand the impact of the current value of 77.6 – I can now see that it is over temperature during occupied hours and the occupant is probably getting uncomfortable. Without the necessary understanding of the data I can’t determine the impact of the current value and its relationship to proper system operation. In order to provide effective analytics I need to “map” this meta-data to point zn3-wwfl4.
 
Interestingly, with all of the power they have gained over the last decade, most building automation systems provide poor semantic modeling of the operational data they contain. The systems provide us with a name and a value but little other information about the specific item. The result is that a labor intensive, process is typically required to "map" the data before any analytics can begin.

Mapping Meta-Data
So how can we capture all of this information and associate it with the data items in our automation systems and smart devices? We cannot do it simply by trying to use standardized point names. Clearly, in even our simple example we have more data that can be effectively embodied in a point name. Add to that the fact that we may want to add numerous other meta-data items over time and it’s obvious we need another approach. There are a number of elements to an effective solution.

        1. Separate the point name from the representation of meta-data. Use tags to represent the meta-data and associate those tags with the point name to provide the semantics that will describe the point.
        2. Utilize a standardized library of tags to provide consistency of meta-data terminology, which will enable automated tools to interpret data meaning.
        3. Follow a consistent naming convention for data points so that the mapping of tags can be consistently applied and automated.

Given our earlier example, a record representing the point with its associated meta-data might look like:

 Point Record

Project Haystack
Helping the industry move forward with meta-data and naming conventions is where Project-Haystack comes in. Project Haystack is an open source initiative to develop tag naming conventions and taxonomies for modeling of building equipment and operational data. The project is developing standardized data models and tag libraries for sites, equipment, and points related to energy, HVAC, lighting, refrigeration and other environmental systems. Substantial libraries of tag names and proposed taxonomy models are already in place.

A key factor in the need for Haystack is the reality that we have millions of points in thousands of systems out there and those point names are not going to change. It’s simply not an option – and it isn’t necessary. What we need is a standardized model for applying meta-data to enable us to associate meaning with those point names. Project Haystack will provide this.

 Project Haystack

Who Should Participate?
Control Solutions, Inc Project Haystack encompasses the entire value chain of building systems and related intelligent devices. Owners and consultants can specify that Haystack conventions be used in their building automation systems to ensure cost effective application of analytics tools (and other software applications), and management of their buildings for years to come. System integrators and manufacturers who integrate Haystack support into their projects and products are positioned for the future of value added services and can streamline the process of turning data into actionable intelligence.

Response to the project has been overwhelmingly positive since its launch in early March. Building owners, equipment vendors, ESCOs, systems integrators, and Governmental organizations are all coming together to help move this essential project forward.

Pragmatic use of semantic naming conventions and taxonomies are essential to make analytics more cost effective and to enable us to and derive value from all of the operational data we now have access to. Please consider joining the effort – its open to everyone interested in the continuing journey of building efficiency. You can check it out and learn how to participate at http://project-haystack.org/


About the Author
John Petze, C.E.M., is a partner in SkyFoundry, the developers of SkySpark™, an analytics platform for building, energy and equipment data. John has over 25 years of experience in building automation, energy management and M2M, having served in senior level positions for manufacturers of hardware and software products including Andover Controls, Tridium, and Cisco Systems. At SkyFoundry he rejoins Brian Frank, co-founder and chief architect of Tridium’s Niagara Framework, as they look to bring the next generation of information analytics to the “Internet of Things”.
 


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