AutomatedBuildings.com Article - Does Building Energy Analytics Really Work?

Introduction

The Internet of Things (IoT) industry is poised to grow to US$ 3.7 billion by 2020, and smart building energy analytics applications are expected to contribute to this growth significantly. Lawrence Berkeley National Laboratory predicts the spending on energy efficiency programs to more than double from 2010 levels to $10.8 billion in 2025, with a possibility to grow to $16.8 billion. These numbers are just pointers to the fact that organizations and governments are going to increase their investments in energy efficiency projects and applications. The Smart Energy Analytics Campaign by the US Department of Energy is an example – this program aims to bring building analytics into the mainstream and enable building owners to operate and maintain their buildings more efficiently.

Can building energy analytics actually reduce your energy consumption and operational costs? Can it improve occupant comfort in the building leading to occupant well-being?

This is a project feedback on how a major hospital in North America used CopperTree Analytics’ Kaizen building energy analytics solution to improve their building performance. While the results of the Kaizen implementation included identification of several potential energy savings opportunities, realized cost savings from energy efficiency initiatives, and improvements in comfort & maintenance procedures, this article focuses on just one of the major findings by CopperTree Kaizen.

CopperTree’s Kaizen solution was deployed in the building in October 2016. In just a matter of one day, Kaizen’s Automated Fault Detection & Diagnostics (AFDD) engine generated insights that pointed to faults in the building.

There are 18 large Air Handling Units (AHUs) in the building, and Building Automation System data from these AHUs are sent to Kaizen for continuous monitoring and analytics. Right after the equipment Trend Log data arrived in Kaizen, Kaizen identified that the AHU Supply Static Pressure Setpoints were not being reset. Even though the AHUs are supplying Variable Air Volume (VAV) boxes, the variable frequency drive supply fans were consistently running at high speeds.

Simultaneously, another AFDD rule monitoring the VAV boxes detected that there were long periods of the day and night operation when the VAV boxes were supplied more air than required (airflow above setpoint). As a first step to evaluate the system operation, CopperTree Analytics proposed a speed reduction strategy on 4 out of 18 AHUs in the building. The supply fan speeds were reduced between 11 pm and 5 am to determine if the airflow requirements would still be maintained.

Fan Speed Reduction

Figure 1: Comparison of Fan Speeds Before and After Recommended Sequence Change

Rules were implemented on the units to determine the total number of hours when the VAV airflows were less than the airflow setpoint while the fan speed reduction program was active. The results indicated that more than 92% of the VAVs were still being supplied enough airflow even when the fan speeds were reduced. For instance, 18 out of 19 AHU1 VAVs were reporting less than 1 hour of operation below airflow setpoint.

This proved that Supply Static Pressure Setpoint reset based on VAV demand could improve the AHU operation while making sure that all the operating conditions were being satisfied. The optimized program would count the number of VAVs short of airflow, and if the total count was greater than the threshold, the Supply Static Pressure Setpoint would be increased. This would ramp up the supply fan speed in order to maintain the VAV airflow requirements. The system would correct itself every 3-5 minutes, and if the VAV dampers are seen to be closing, which is indicative of reduced need of airflow, then the Supply Static Pressure Setpoint would be reduced. To summarize, the optimized program implements a Supply Static Pressure Setpoint reset strategy that continually monitors airflow requirements of all VAV boxes supplied by the AHU.

CopperTree proposed that the Supply Static Pressure Setpoint reset strategy be implemented on AHU1 as a proof of concept project. The operations team at the hospital agreed to implement this strategy, and the control program was optimized on March 29, 2017.

Figure 3: Supply Static Pressure Setpoint Before and After Implementation of Reset Strategy

It was important to measure and verify the results from AHU1 before optimizing the program for the remaining AHUs. CopperTree Kaizen’s Energy and Reporting features were used for Measurement & Verification (M&V). Energy and Cost Analysis rules were implemented to calculate the AHU consumption and compare post-optimization consumption with the baseline. Figure 4 below shows the realized savings (see CUSUM Cost Savings Trend Log) for AHU1 from the first two weeks of operation since optimization. The results indicate that the hospital is expected to save approximately $6000 annually from just one AHU.

Figure 4: Realized Cost Savings from Implementing Supply Static Pressure Setpoint Reset Strategy on AHU1

After verifying the success of the project on AHU1, the hospital is now moving ahead with the recommendation to implement this strategy on all of the AHUs in the building. The estimated cost savings from this measure on all the AHUs combined is approximately $50,000 annually. Added to this energy saving measure, CopperTree Analytics’ Kaizen has identified several other opportunities to save energy, reduce maintenance costs, and improve occupant comfort in the building.

Now, back to the questions. Can building energy analytics reduce your energy consumption and operational costs? Can it improve occupant comfort in the building which in turn results in occupant well-being? Although we did not cover how building analytics could be used for improving occupant comfort, the answer is an emphatic Yes! Additionally, once the optimization measures are implemented, and faults in the building are addressed, building analytics ensures that equipment operation does not revert back to the “old ways.”

Over and over again, our clients from around the world are making the most of the features in CopperTree Kaizen to improve their building performance and operation significantly. Building energy analytics will play a very important role as we march towards a sustainable future.

About the Author:
Derek John Mullassery is a Building Application Engineer at CopperTree Analytics. He holds a Master’s degree in Clean Energy Engineering from the University of British Columbia, Vancouver and an undergraduate degree in Electrical Engineering from India. Derek’s interests include data analytics for building performance improvement, controls optimization, energy management, and behavioral change programs for occupants and operators.

Additional information about CopperTree Analytics:
Website: www.coppertreeanalytics.com
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