The Student Union at the University of California Los Angeles (UCLA) is one of the largest student unions in the world. The two adjoining buildings, comprising more than 200,000 square feet of retail, meeting, and administrative space, are owned and operated by the Associated Students of UCLA. ASUCLA was uniquely established in 1919, when UCLA first opened its doors.
Working with Pario Asset Advisors, ASUCLA invited BuildingIQ to assess the potential for energy savings in its core complex, which is situated at the heart of a sprawling campus that serves over 43,000 students.


The key challenges being addressed are both technical and institutional. The buildings are more than 30-years old, and the HVAC system is out-of-date and inefficient.

It has been operating with older controls and without the benefit of variable frequency drives (VFDs), a prerequisite of modern building automation.

From BuildingIQ’s perspective, the building must be made ready before the Predictive Energy Optimization™ (PEO) platform can be employed to take energy efficiency to higher levels.

Preparing a building for PEO to function as a comprehensive solution requires close coordination and collaboration from all parties involved. At UCLA, BuildingIQ and Pario are managing the project endtoend. The “make-ready” of the building has been a combined effort by the in-house Facilities team, Pario, and the BuildingIQ Managed Services Team. Managed Services includes an ongoing service function, after installation is completed, that provides 24/7 monitoring, identifies inefficiencies, and continually optimizes the building’s energy efficiency to achieve and maintain peak performance.


Pario’s long-term relationship with UCLA’s Institute of the Environment and Office of Sustainability provided the initial opportunity for BuildingIQ to meet with ASUCLA and university administration. BuildingIQ proposed an integrated solution that brings the building up to the level necessary for advanced automation, and then undertakes the PEO learning and optimization process.

Finally, it sustains a long-term servicing relationship with ASUCLA to ensure peak performance is maintained.
The technical solution involves three major steps.

First, BuildingIQ is already working closely with the University staff to install variable frequency drives on the fans that distribute the air, and to integrate those drives into the new BACnet infrastructure. Second, to ensure that the building management system (BMS) is BACnet-enabled, the existing Siemens 600 system will be “translated” into BACnet, and then mapped into the BuildingIQ platform. The third step consists of establishing an Internet connection to ensure secure remote access.
Once the building is technically ready, the PEO platform will go through the process of learning the building’s thermodynamic behavior before active control begins.
Optimization is the next step. Using BuildingIQ’s proprietary algorithms, the system will find the strategic path to multi-objective optimization (i.e. cost and energy).


Barring unforeseen difficulties, the expected timeline calls for 3-4 months to install the critical equipment (e.g. VFDs) and to establish system connection and control; followed by a one-month learning process, when the PEO system learns the building’s unique thermal behavior. BuildingIQ will start optimizing by winter 2015; initial results will be available in early 2016.

BuildingIQ anticipates considerable energy savings up front simply from the installation of the variable speed drives and other equipment in the preparatory phase.
Overlaying BuildingIQ’s infrastructure on top of that will further enhance savings for the client.

ASUCLA provided three years of historical overall building electrical monthly consumption for the building. But, the building is so old that metered consumption data for the HVAC’s energy usage was not stored. Therefore, savings calculations will be hindered by the lack of a historical baseline. BuildingIQ will analyze the data to get a rough estimate of energy consumption levels and patterns for initial comparisons. The onemonth learning process will then be used to establish an initial set of interval baseline energy consumption data. This will provide a preliminary data set to show the before-and-after improvements made through PEO. Furthermore, HVAC power meters are being added to help optimize the PEO technology and to provide realtime, HVAC energy consumption data going forward.

An update to this case study will be done once the results for the Optimization phase are available.


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