The LOBOS^TM (LOad Based Optimization System) was created and refined over a 25 year period to meet the comfort control and energy conservation needs of the building operations and facilities management professions.  It is intelligent HVAC software that is changing the way large commercial facilities consume energy and participate in demand response, while dramatically increasing occupant comfort.  The Energy Efficiency (EE) optimizes the chilled water and air handling unit systems together, providing a very stable reset platform for the chiller plant.

As the name implies, the software uses end use cooling loads and resets the control system variables in response to what those loads actually require.  Beyond simple resets that address only the chilled water, or just the air side of the equation, it also deploys sophisticated sequences of operation that balance the operation of all cooling system resources, including chillers, air handlers, and pumps, saving energy while simultaneously improving tenant comfort.

Early on in development the problem of data normalization coming from each Energy Management System (EMS) that the software connected to was solved by building on the Niagara^TM framework.  We knew that if the integrators followed our point models, it would function as expected.  Then a similar problem was encountered when integrating into Analytics applications.  Because we wanted to keep things simple for our integrators, it was necessary to keep the point count only to what was required for our product to function and we were very hesitant to add to our point count in order to just support a new application.  Ideally, a method was desired where the points that the integrators were bringing in could describe themselves to the software and then it could automatically route the incoming data.

While LOBOS^TM was being deployed, we realized that having a method to quickly determine if our integrators had completed the mapping to the EMS appropriately and if the program was functioning properly in the installed environment was going to be key to our success.  In the beginning, this was a very manual process, as it just handled this through the traditional commissioning process.  Traditional commissioning was very time consuming and labor intensive, so Enerliance-Yardi began developing a Fault Detection and Diagnostics (FDD) product that could help with both the commissioning process and with functional analysis as well.

FDD brought about the data normalization issue again, as well as a transport issue.  How would LOBOS^TM EE get the data out of Niagara^TM and into FDD and how do we minimize the effort necessary to make that happen?  Project Haystack, and in particular, the nHaystack Niagara^TM service solved both problem sets for us easily.  Utilizing the nHaystack service, all of our existing points in the Niagara^TM environment had Haystack tags added and because the points that are tagged were standard on all of our builds, we had everything necessary to export data to any external application that utilizes the Haystack standard.  It didn’t matter if it was a single piece of equipment or a thousand pieces of equipment.  Everything was automatically Haystack tagged and ready to be utilized by third party applications, in our case, our FDD.  Because the nHaystack service implements the ReST API specified by the Haystack standard, it provided an easy way to not only describe the data that was in the system, but to also retrieve that data from the system.  And that’s exactly what FDD does, it retrieves data via ReST calls to the nHaystack service, enabling us to bring an entire site on-line in FDD by simply pointing it at the location of a Haystack compliant set of data and providing the appropriate credentials.  Because the data is self-describing our system knows what the data is, where it belongs, and what to do with it.  There is zero human interaction necessary to build a site.

[an error occurred while processing this directive] Utilizing Haystack to deploy FDD solved a myriad of problems.  Had we not utilized Haystack, integrating each deployment of LOBOS^TM with FDD would have been a manual, time-intensive process that would require commissioning once complete.  By utilizing Haystack, we only have to test that the Haystack tags are correct and that FDD also recognizes those Haystack tags correctly and it only has to be done once.  We know that if the configuration is correct, then each and every deployment will also be correct.  This is a massive time savings and eliminates the chance for errors during the deployment process.  As a result, we can deploy FDD in conjunction with LOBOS^TM EE or Demand Response (DR) with little, if any, added expense.  This keeps our project costs down and allows us to provide additional products to our customers with little up-front investment.

About Enerliance: Enerliance, founded in 2005 and acquired by Yardi Systems in 2014, is the company behind the Load Based Optimization System (LOBOS), an intelligent optimization system for large-scale air conditioning systems that offers improved comfort, energy efficiency and fully automated demand response capability that improves bottom lines for building owners and occupants.

LOBOS delivers more than 32 megawatts of fully automated demand response capability to the grid along with more than $5 million in annual energy savings. 

Enerliance has earned numerous industry accolades, including recognition by CIO magazine, one of the most highly regarded publications in the information technology industry, as one of the 20 most promising utilities solutions providers in 2013.  For more information, visit www.enerliance.com.

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