Data analytics allows us to review detailed information on equipment operation in a way that historically was not possible. In the past, a maintenance department could use technology to see how their mechanical systems and equipment were operating on that day, or maybe how it operated over several days previously. Now, using data analytics, we can see how the equipment has operated since the beginning of the energy performance contracting project.
The power of data analytics was recently shown in our work with the City of Gladstone on their energy conservation project. We were able to significantly reduce the energy consumption at the Gladstone Community Center by fine tuning the different mechanical systems.
Historically there have been areas of the Community Center that tend to be over-cooled while other areas are under-cooled, which resulted in comfort complaints from the Center’s patrons and employees. Figure 1 shows the layout of the Gladstone Community Center and the heating/cooling zones for each floor.
Figure 1: Gladstone Community Center Floorplan
Areas like the gym and fitness floor always have a large cooling load, even in the winter, from people working out. But the office area located next to the gym has a much smaller cooling load than these other physically active areas. The units that serve these areas (RTU 1 and RTU 3) supply cold air between 55°F – 65°F year around. Each zone is equipped with a damper to control the amount of air entering the space and an electric heating coil to reheat the cold air as needed to maintain the desired zone temperature. Unfortunately, re-heating the cold air is very inefficient and wastes energy. It also requires a large amount of electricity, which can be very expensive.
To address this over/under cooling problem, our optimization team for the City of Gladstone project used data analytics to analyze the rooftop and each zone’s operation. Once the problem areas were identified, adjustments were made to reduce the electric reheat, thus dramatically reducing the energy consumption. This had to be done methodically to avoid damaging the rooftop units and maintain needed air flow. The following figures illustrate how the adjustments took effect.
Figure 2 shows the electric demand (kW) for RTU 1 and the re-heating status of each zone during the last week of June 2018. A color mark in the lower graph indicates that the zone’s heating status was on. Notice in the upper graph that the electric demand averages around 40 kW per day. These two graphs show that a large number of zones are using reheat.
Figure 2: Gladstone Community Center RTU1 kW Demand (June 2018)
Figure 3 shows the same graphs as Figures 2 but for the last week of June 2020. Notice the average daily kW is now only around 10 kW and the number of units using reheat is limited to just a few offices.
Figure 3: Gladstone Community Center RTU1 kW Demand (June 2020)
If you compare the demand graphs side by side (See Figure 4), you can see the average daily kW, which was 40 kW, has been reduced by 75%! This change has saved the City of Gladstone over $1,500 in cost avoidance savings.
Figure 4: Gladstone Community Center RTU1 kW Demand – June 2019 vs. June 2020
About the author – Devin Klish is an Energy Manager with Navitas. Through the use of Data Analytics and other proprietary tools, he works to educate owners, occupants, and operators to further optimize facility operation and ongoing initiatives.