The following Navitas article was published in the Summer 2018 issue of the Missouri School Plant Manager magazine, published by PTR Communications. The Missouri School Plant Manager magazine is the official publication of the Missouri School Plant Manager’s Association (http://www.mspma.com). If you would like a PDF of the article, click here.
Building owners and operators are all eventually challenged with aging facilities. Often patrons or customers of those buildings are focused on the aesthetics and functionality of the interior space, but operators know aging facilities present more challenges that just the aesthetic. I have heard many school administrators lament over community members that oppose school building projects, especially when a building must be abandoned. The reasons for this are wide-ranging and mixed, from genuine financial concerns to pure nostalgia for the building.
Regardless, wherever community concern is generated, we in the facility management industry are often tasked with recommending and justifying decisions made around building projects – many of which an average patron of the building will never recognize. One way we have found that can help justify actions and actually save money at the same time is through the use of data analytics. The added insight the data provides on how a building is operated can justify decisions on how a building is run, diagnose comfort concerns from occupants, identify humidity issues, improve and optimize maintenance plans, etc.
Additionally, in the last few years, the ability for facility managers to collect and use data have helped many school districts decrease their utility consumption by 20-35%. While energy costs may not be top of mind to most school building operators, what benefit could the decrease of payments to the utility return to your school? What flexibility would those savings provide you on facility projects?
The use of data analytics has greatly helped building operators understand what is happening in their building. By gathering data points in real time, operators can understand how a building consumes energy. With that information, informed decisions can be
made on how to decrease energy use while maintaining comfort for building occupants.
Take the example, shown in Figures 1 & 2, of a building before and after optimization. Figure 1 shows a building with a typical spike in electric consumption during the day and decreased usage in the evening. However, this building’s temperature set points maintained 70°F all weekend instead of going into unoccupied mode as intended.
1. Temperature set point was inadvertently changed to 70°F all weekend.
2. Heating units stagger during the weekdays but are on most of the time over the weekend.
3. As a result of increased usage, electric consumption increases dramatically for the weekend. This consumption can be quantified to determine the financial impact of this one building automation decision.
Figure 2 depicts the graph of the electric consumption after building optimization. Here you can see a dramatic decrease of electric consumption over the weekend.
4. Room space temperatures gradually decrease until reaching 60°F, then are maintained at 60°F until Monday morning.
5. Units on/off status. Note the units stagger on and off to maintain temperature as needed.
6. Electric consumption is very low during unoccupied hours, with a few spikes to account for units firing to maintain 60°F.
We have seen school buildings operate in both of these scenarios, as well as unintended operation of buildings during snow days or holiday breaks. Aside from the difference in energy costs, there is the potential to decrease maintenance costs as well. Upon implementation of data analytics, one school recognized some inefficiencies in their system, and made changes that changed their equipment run times from 7,000 hours annually to 1,000 hours – a decrease of 85%! If equipment could effectively run that much less, how much would your school district save in maintenance? How many fewer failures and service calls would be required? How much longer would your equipment last? How much could your finances be impacted if equipment performs better & lasts longer?
Data analytics is also helping facility managers identify hard to diagnose issues. Take the graph shown in Figure 3 as an example. This graph depicts actual electricity consumed over ten days. The first two days show the electricity consumed over the weekend at an average of 43 kW. As staff arrives on Monday morning, electricity spikes up as lights, computers, coffee pots, etc. are fired up, then the electric consumption drops back to around 43 kW at night. This pattern is consistent for about a week, but for a building this size we would expect the overnight electric consumption to be significantly less.
We noticed this abnormally high consumption and contacted the facility director. We determined that while the building automation set the temperatures back at night, the vestibule cabinet unit heaters had a high temperature set-point, and essentially were attempting to heat the entire building during unoccupied hours. By simply adjusting the thermostats in the cabinet heaters, the building reduced the baseload by about 29 kW, resulting in over $2,000 per month saved on their electric bill. Without data analytics, this problem may never have been diagnosed.
Other problems we’ve found through data analytics that might never have been diagnosed include:
- Heating and cooling at the same time
- A snow melt system powered on during the summer
- Summer and winter night setback temperatures swapped (during winter unoccupied hours, the building would heat to 80°F instead of maintaining 60°F
- How one room remaining in occupied mode overnight can cause a large cooling tower with over 30 hp of pump and fan motors to remain in operation
- Wireless thermostats losing communication, which caused heat to be engaged during cooling months
- How cleaning staff pushed every override button in a building over a 20 minute period every night, causing a spike in electricity demand, and a 400 ton chiller to be unnecessarily be engaged after 11:00 at night.
- How much it costs the district to operate HVAC equipment in a gym for the community men’s Sunday basketball league.
The role data analytics can play for a school district is partly to catch errors and inefficiencies, but also to show the district how much it costs for certain activities or behaviors. From there, district personnel can make informed decisions on how their buildings should be operated, or how much they should be charging for the use of facilities.
What could data analytics show about your buildings? What is happening inside your buildings when they’re unoccupied? Could a platform like this help you diagnose issues you think you have? Or issues you don’t yet know about?
Ultimately, energy savings strategies like the ones discussed in this article can play a part in funding the renewal of your buildings. This can allow you to keep as much money funneled into education as possible, instead of maintaining old, inefficient equipment. Talk to your energy services provider and consultants to determine how data analytics can be of value to your district.
About the author – Ryan Terry is a business development manager with Navitas. His background as a professional engineer and 15 years of experience in the energy industry help him bring a practical approach to developing strategies for public sector clients who want guidance in how to initiate an energy conservation program in their facilities. You can email him at rterry@navitas.us.com.