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Data Analytics and the Renewal of Buildings


Article by: 
Ryan Terry, PE, CEM
Business Development Manager


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 or 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 top of mind to most school financial officers, 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 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. 

Data analytics is also helping facility managers identify hard to diagnose issues.  Take the following graph as an example.  This graph depicts actual electricity consumed over ten days.  The first two days depict 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:

  • 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 of 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, using data to drive energy decisions as well as implementing energy savings strategies 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.  Consult with your energy services provider and consultants to determine how data analytics can be of value to your district.