Accurately Predicting Boiler-Controller Energy Savings

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More heat was required during the first hour following a transition to inactive mode because the return to a higher average water temperature required the addition of heat. Figure 4 shows the temperature of water in the system changed with the change in operating mode. In addition to the water in the heating loop, the thermal mass in contact with the water (pipes, insulation, heat exchangers, walls in contact with pipes and insulation, etc.) was undergoing changes in temperature and stored energy. Upon the transition to active mode, the return to a lower system temperature allowed heat to be removed from the thermal mass of the system, so less fuel was required to deliver the necessary heat to the load.

The ?Inactive ? active? data trace in Figure 6 supports the observation that what happens the first hour following a mode change is not ?normal operation.? At the test site, there was no reason for a dramatic change in heat demand in the hours around midnight. Building demand between 11 p.m. and midnight should have been similar to building demand between midnight and 1 a.m., while building demand between midnight and 1 a.m. should have been similar to building demand between 1 and 2 a.m. If the active and inactive results between midnight and 1 a.m. were averaged, Figure 6 would show a slow and consistent downward trend in demand between 10 p.m. and 5 a.m. Instead, Figure 6 shows a significant difference in demand that occurs at midnight and depends on whether the boiler controller is active or inactive. This is not a real change in demand; it is an artifact of the test method.

To show how the difference in heat delivered to a load affects fuel use, all active-day and inactive-day gas-usage data were averaged in 2-min increments. The 24-hr plot of composite active-day and inactive-day gas usage shown in Figure 7 looks almost identical to the 24-hr plot of composite active-day and inactive-day temperature drop across heating load in Figure 6. As in Figure 6, the biggest difference is seen between midnight and 1 a.m., which is attributed to the change in energy storage occurring with each mode transition.

For each 1-hr period, fuel usage and the heat delivered to the building were totaled. The hourly totals for all active days were summed and then averaged by dividing by the number of days. The same calculation was done for all inactive days. The differences in hourly fuel usage and heat delivered were calculated by subtracting the active results from the inactive results.

Figure 8 shows the difference in fuel usage occurring the first hour following a mode transition represents 34 percent of the total difference in fuel usage for a full day of operation. This difference was caused by the change in system temperature occurring immediately after a mode transition shown in previous plots, an artifact of the test method.

What the Analysis Tells Us
The analysis shows that nearly all of the fuel ?savings? measured after transitions from inactive to active controller mode are not savings, but an artificial product or effect resulting from a change in the amount of energy stored in the system. Although the size of thermal-storage terms differs by job site, in all cases in which a boiler controller blocks calls for heat and does not change the operating setpoint, the alternating-day test method will overpredict fuel savings.

While much of the analysis focused on the difference in performance the first hour following a mode transition, changes in thermal storage may take longer to capture, depending on the portion of total fuel use thermal storage represents.

The results presented in this article required the recording of multiple channels of data at 2-min intervals for approximately seven weeks. This level of data acquisition and the subsequent analysis provided the information needed to separate energy storage from energy savings. While this approach could be duplicated, it, unfortunately, is beyond the scope of most end users. A method requiring minimal data logging, avoiding the thermal-storage error inherent in the alternating-day test method, and providing an accurate prediction of energy savings is needed.

A Better way to Measure Energy Savings
The following approach avoids the error introduced by the alternating-day test method:

• Follow a two-days-on-two-days-off, rather than a one-day-on-one-day-off, cycle of boiler-controller operation. Repeat this for the entire test period.
• Record fuel usage for every 24-hr period of operation. For example, if a mode transition is scheduled to occur every two days at 2 p.m., record fuel usage for every 2-p.m.-to-2-p.m. period for the duration of the test.
• Record fuel usage the first day following a mode transition (Day 1, Day 3, Day 5, Day 7, etc.), when data will be affected by the accompanying change in thermal storage and fuel savings will be overpredicted, for post-test-analysis comparison.
• Record fuel usage the second day following a mode transition (Day 2, Day 4, Day 6, Day 8, etc.), when the mode-transition effect on fuel use will be reduced significantly or eliminated, for a more accurate estimate of future energy savings.
• Record data for as many days as feasible, as the greater the number of days, the greater the accuracy of predicted savings. At a minimum, one should have eight weeks of total run time, which provides four weeks (the second days following mode transitions) of ?usable data.?
• If results seem unsatisfactory or inconclusive, continue testing using the same method.

Summary
Prior to the purchase of an add-on boiler controller to reduce fuel use, end users should be motivated to predict as accurately as possible their future savings. As shown in this article, the choice of test method is important. One has a better chance of selecting a proper test method by possessing a basic understanding of how the controller works. Controllers that change the temperature of water directly by modifying the setpoint or indirectly by blocking calls for heat change the amount of energy stored in a system. Failure to account for changes in energy storage leads to an error in predicted energy savings. A simple test method that avoids this error and provides a more accurate estimate of energy savings was proposed.

Acknowledgement
The author wishes to thank Scott H. Smith, PhD, and Jim Gotterba, PE, MBA, of ALZETA Corp. for their support in collecting and analyzing data for this article.

The president of ALZETA Corp., John D. Sullivan is a registered professional engineer in the state of California. He has more than 30 years of engineering experience in combustion, heat transfer, and related fields.

Did you find this article useful? Send comments and suggestions to Executive Editor Scott Arnold at scott.arnold@penton.com.

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