- for each HVAC system, the duty cycle while in heat mode and while in cool mode as a percentage of the day
- the amount of kilowatts saved per room per day in heat mode and in cool mode
- for each HVAC system, the amount of runtime per day in heat mode and in cool mode
TMI?
Is this exponentially more information than you ever cared to know? Yep, we get it. Just because we CAN gather this data, does it mean we SHOULD?
In and of itself, the answer may be “no”. But as they say, it’s not what you’ve got, it’s what you DO with it.
What Can You Do with This Data?
The raw data in and of itself has limited value. It becomes a powerful tool when that data is analyzed for you and presented in ways that make it easy to understand. One way to present such data is in pie charts and color coded graphs.
Another way to present that data is to frame it along with corresponding data, like these examples:
HVAC Forced Off
Devices which run non-stop are forced off at a certain point. There are a number of reasons a device can be forced off. For example, it might be an indication of a device that is failing, or of a patio door that was left open.
Here are some examples of how HVAC forced off data can be analyzed:
- Averages the Forced Off Time per device.
- Presents an overview of Forced Off time per device.
- Specific devices that were forced off are displayed.
- Highest 10 Forced Off Devices
Runtime Data
Here are examples of how runtime data can be analyzed:
- Lists devices which differ significantly from the average value.
- Lists the devices with the highest and lowest runtimes.
- Displays number of hours these devices ran during the last 30 days.
- Indicative of possible maintenance or repairs needed.
How can you act on this data analysis?
Visit the rooms listed on the report, investigate the possible causes (for example, dirty filters). We recommend viewing this report on a scheduled weekly basis.
Setpoint Differential
Here are examples of how setpoint differential can be analyzed:
- Differential distribution for devices in occupied rooms
- Room temperature and setpoint distribution
- Occupied devices that are too cold and too warm
- Differentials trending poorly
- Fluctuating differentials
Rooms by RH%
Here are examples of how relative humidity data can be analyzed:
- the number of rooms in the sample
- the number of devices with relative humidity sensors
- the average relative humidity
- Provides a detailed list of rooms, their devices and the relative humidity
So, you can see that by presenting facts and figures in relation to-or in combination with-other factors, the data that an EMS can generate becomes infinitely more valuable.
Visit us again next week to read about alerts that can be configured in EMS systems.
Choose your next energy management system wisely. To learn more, read our white paper, How Energy Management Systems Work.
