Commercial Case Study


Project Overview

Putney Central School, K-8 grades, is located in Putney, Vermont.    The school has three wings constructed in 1955-64, 1974 and 1996 heated by three hot water boilers.    Each wing is a separate heating zone identified as zones A, B and C.   

Data loggers were deployed on each boiler oil burner to determine daily operating time.   Fuel splits were calculated by multiplying the burner operating time by the burner nozzle through put and the heating value of fuel oil.   This fuel split was then converted to Btu/Square Foot (SF) ‘ Year (Yr) to determine the relative heating efficiency and fuel consumption in each of the three zones.

The fuel split was further converted to Btu/SF-Yr-Heating Degree Day (HDD).   This calculation showed the relative efficiency of heating adjusted for HDD.    If the number found by this calculation is less than five, then the building heating efficiency and envelope thermal efficiency are good.    If the number is greater than five there is room for improvement.   Further investigation is needed to determine the contributing factors to higher numbers.

An energy audit was performed on the Putney Central School in 2008.   The building energy performance was computer modeled using Trane Trace 700 software.    The zone energy splits between the computer model and those determined by data loggers were compared.    The computer model used with the associated assumptions was found to inaccurately predict building performance.    The computer model varied from the data logger energy use by 7 to 42 percent.


Data Logger Deployment

Motor run time data loggers were deployed on boilers serving heating zones A, B and C at Putney Central School on April 1, 2009.    Data loggers were removed on May 7, 2009.

Boiler A serves the gym and a front wing built in 1957 and 1964.   Boiler B serves a middle school built in 1974 and Boiler C serves a wing built in 1994.   Boiler A has a one minute purge cycle at the end of firing, which was subtracted from each burner cycle time.

Data Analysis

Analysis of the data was done for the coldest day during the period, April 13.   The day had a high temperature of 39.3o F. and a low temperature of 31.0o F.

The use of oil for April 13, 2009 is given in Table 1.
















Table 1, Fuel splits between heating zones, April 13, 2009

Extrapolated over the year with annual oil consumption of 14,500 gallons the fuel oil split and heat loading by school zone is given in Table 2.



Annual Gallons

Zone Square Feet

























Table 2, Heat loading by school zone

Note: Square Feet is the area of the heated space served by the boiler; Sf is square foot; Yr is year; HDD is yearly heating degree days.   For Putney, HDD is based upon 7,200 annual heating degree days.   Btu/Sf/Yr/HDD less than 5 indicates an efficient heating system.   Btu/Sf/Yr/HDD greater than 5 indicates improvements can be made to the heating and building envelope system.

Data Interpretation

Boiler B in the middle school had the highest heat load per square foot of floor area at 16.7 Btu/Sf-Yr-HDD.   The area served has a high outside wall area for the floor space and contains an entry corridor with a large glass area.   The boiler and overall heating efficiency appear low.

Boiler A serving the gym and front wing had the next highest heat load per square foot of floor area at 6.9 Btu/Sf-Yr-HDD.   The boiler was oversized for the heat load and replacement should be considered.  Cavity Wall Insulation, ventilation and air handling unit upgrades should be accomplished before the boiler is replaced.

Boiler C, serving the new wing, had a heat load at 3.6 Btu/Sf-Yr-HDD that reflects better insulation, thermal pane windows and a heat recovery ventilation system.   This boiler appears to be oversize.    Consideration should be given to using Boiler C to provide the heat for the Boiler B area and removing Boiler B.   Verification of Boiler C run times during the coldest part of the year should be done before heating system modification.

Data Comparison to Computer Model

The school was modeled using Trane Trace 700 software.    A comparison of actual building performance with the model is given in Table 3 with annual fuel splits in percentages.   Variance compares the data logger actual performance with the modeled performance.


Modeled Fuel Split %

Data Logger Fuel Split %

Variance % Over (Under)













Table 3, Comparison of Modeled School Performance to Actual Performance

As can be seen from Table 3, the modeled building performance by zone varies from actual performance by 7-42 percent.   Thus, the model as used in this example is an inaccurate tool in predicting building performance.    Energy conservation measures based upon the model would not reflect current and future building performance.


Data loggers were deployed to determine the fuel splits for three heating zones in a local school.    Results were converted to a measure of building heating efficiency of Btu/SF-Yr-HDD.   The zones varied from 3.6-16.7 Btu/SF-Yr-HDD.

A number of less than 5 BTU/SF-Yr-HDD indicates a relatively efficient heating system and thermal envelope.    A number of greater than 5 Btu/SF-Yr-HDD indicates improvements can be made in the heating system and building thermal efficiency.   The low number found at the school of 3.6 Btu/Sf-Yr-HDD was in a new wing constructed in 1994 with efficient insulation package, thermal pane windows and heat recovery ventilation.    The high number of 16.7 Btu/Sf-Yr-HDD was found for a school wing with large amounts of glass, an entrance door with poor weather stripping, an inefficient boiler and minimal insulation package.

The actual building heating performance found through data loggers was compared to computer modeled building heating performance.    The computer model was found to be off by 7-42 percent compared to actual performance.

Data loggers provide a simple and accurate method to determine actual building performance.    They can be used to obtain building performance baseline data.   From this baseline data energy conservation measures (ECMs) can be proposed with real building information.    Reductions in energy use from installation of ECMs can then be measured and verified through subsequent data logger deployment.

<i>About the Author

Craig Stead has been working in the energy field for over 30 years.   His company, Stead Energy Services, focuses on energy efficiency and conservation for schools, wastewater plants and other commercial structures.


Cornell University, Ithaca, NY, BS, Masters of Engineering (Chemical) 

Western New England College, Springfield, MA, MBA

Building Performance Institute, building analyst training

Association of Energy Engineers; comprehensive training for energy managers

Professional Certifications:

Professional Engineer, Civil & Sanitary, VT, MA

Building Performance Institute, Certified Building Analyst

Association of Energy Engineers (AEE), Certified Energy Manager

Heat Spring Institute, Certified Heat Pump Installer</i>


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