Accurate HVAC Comparisons Require Time and Effort
Even to people with no HVAC knowledge, it is obvious that the large air conditioning units in stores, factories and other large single zone HVAC applications consume massive amounts of energy. It's also intuitive that the more efficient each unit is, the lower the energy cost. However - even among HVAC industry engineers - accurately projecting these savings is a complicated endeavor. It can be done but requires a considerable amount of time and effort.
When it comes to computing energy efficiency, a familiar calculation is changing lighting from fluorescent bulbs to LEDs. The light is either on or it is off. If you know how many hours you run the lights, how many are on at any given time and electrical rates, you can plug the figures into Microsoft Excel and accurately model your savings in just a few minutes. And - importantly - climate conditions are not a variable with which you have to contend. A light on in Phoenix in August uses the same amount of energy as a light on in Boston in January.
HVAC energy consumption is on the other end of the consistency spectrum. While lighting demand will be consistent from week to week and year to year, air conditioning system performance is extremely variable and dependent on both outdoor and indoor conditions. Complicating things even further, these conditions that affect HVAC performance are subject to change not just over large swaths of time, but vary significantly within the same day, even within the same hour.
For example, if you have an HVAC unit set to maintain a specific indoor temperature and the ambient temperature goes up, the capacity and efficiency of that unit will go drop simultaneously. Of course, the opposite is true - if the temperature goes down the unit becomes more efficient and its capacity increases. Therein lies the challenge: as conditions fluctuate in real-time during the day, so do the unit's capacity and efficiency.
And that's not the only moving target - when the outdoor humidity varies, then the fresh air that infiltrates the building is also going to affect the humidity in the space. Added to that variability, you have occupancy changes, variable computer loads, and other changing internal loads. Taken in total, it's the kind of variable chaos that will challenge even the most complex regression curve and defy a basic spreadsheet explanation.
In my own experience, I've only found one way to get truly accurate real-world comparative data on HVAC performance. It is a painstaking process, but it is also effective.
- 1. Level the playing field before the game
The first thing to do is get the units as close to optimal operating condition as possible before beginning any test. That is true whether the unit is being re-commissioned or is straight from the factory. This is a crucial step in evaluating the effectiveness of any energy saving upgrade or retrofit. It's one of the few variables that can actually be removed from the equation, so it's critical to do it from the outset.
A retrofit test that does not segregate unit re-commissioning savings from the savings of the retrofit itself will be a fundamentally flawed test.
- 2. Lots of data points, even more patience
Next, we attach a wide range of sensors that log just about every variable of the system. This allows monitoring of subtle changes. During a test we will run the entire system for a week in retrofit mode and for a week in standard or "pre-retrofit" mode (more on that later). We alternate between the two for an entire year which gives a highly accurate representation of how that unit is going to respond in a typical year and also how the pre-retrofit mode and post-retrofit modes compare.
- 3. Test the whole space, but treat each unit as an individual
In an installation with multiple units in operation within the same thermal envelope it is important to gather data from each unit individually, so that performance of each unit is only compared to itself. If a thermal envelope is served by multiple units the differences in load profiles among the units can frustrate accurate data collection unless they are monitored separately, then added together to project the performance of the total thermal envelope.
- 4. Testing intervals are critical
The one week on/one week off interval is not an arbitrary interval. On/off intervals of shorter duration in cooling mode will blur the results if there is a dehumidification load. A retrofit that reduces the relative humidity will typically spend the first day working overtime to limit the relative humidity. Conversely, the first day in standard mode is a cakewalk, as the humidity in the space is lower than normal which significantly reduces the amount of latent heat that has to be removed by the unit.
Most importantly - we do that for an entire year. Extrapolation is a dangerous tool, even in the hands of statistical experts, when it comes to HVAC. While some climates may be more uniform, we did much of our early testing in climate zones three and four which experience four very distinct seasons. It was a hard lesson, but telling- we could only reliably predict savings after a full year of testing. The full year test also revealed the idiosyncrasies of space usage. Retail space, for instance, usually sees an unusually high internal heat load in November and December during the heavy shopping season. School savings are affected when child care services are offered onsite after hours and in the summer.
Computer modeling can be a good approach if you feed the program accurate real-world data. However to get good data out, you'll need to add lots of good data in.
First off, input the building parameters: size of the building; the lighting intensity; lighting schedules; personnel schedules; R values of the insulation; and anything else that might impact HVAC performance. Then add HVAC data including power and efficiency of the units in pre-retrofit mode. Actual operating performance for each unit should be field-measured and entered into the program. And then for the finale, have the program access ten-years of normalized weather data to create a representative 24-hour day for each month. Then simulate the building and HVAC equipment using the weather model.
Then - after all that effort - your simulation is only as good as the accuracy of the HVAC retrofit model. If a retrofit uses a new methodology, it is unlikely that an accurate model exists, so a new model would need to be developed. Assuming the new accurate equipment model magically appeared, the comparison of pre-retrofit and post-retrofit data projections should be fairly accurate. The absolute accuracy, on the other hand, would not be nearly as reliable.
Bottom Line: Any way you attempt it, projecting HVAC performance is a very complex undertaking. Any claims of retrofit energy savings should be accompanied by a detailed account of the testing methodology and assumptions.
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