According to the DOE and the EPA, on average commercial buildings waste approximately 30% of the energy they use. If you want to reduce your energy consumption and costs and are NOT using interval data for energy management, then read on and you will learn exactly why real time and interval data are critical data sources to identify savings opportunities.
Interval data is generated by interval meters and/or smart meters, which track your energy usage and is generally available 24 hours later. You can access your usage and demand in 5 to 60-minute intervals, depending on the utility. Utilities in many states are required to provide their medium and large sized users with an interval meter; however, it is still very useful for smaller users. In addition, some utilities like PG&E and Southern California Edison have downloadable raw interval data straight from their online customer portals. Whether your annual consumption is 1 million or 20 million kWh, decreasing your usage and costs and being fully aware of how your building is functioning is always resourceful.
Interval Data vs. Real-Time Data
Interval data usually lags by 24 hours collected some time interval the previous day (e.g. 15 minutes, 60 minutes, etc.). Real time data is available a second later. You will be able to look at what is going on in your building right now. Would you want to wait an entire month to realize that your usage last month was higher than normal? That is what happens with normal utility bill data. Then, you still do not know what exactly happened. Were there spikes at specific times of the day that could easily be avoided? Were lights and computers left on when they should have been powered off? You could be wasting tons of energy AND money by not knowing what is going on in your building. Whether you have real time or interval data, having IDR (Interval Data Recorders) data is much more efficient in managing a building than monthly utility bill data.
Benefits of using IDR Data
Utility bill data is at least one month delayed and lacks granularity- you receive one kWh number (sometimes on and off peak kWh) and one kW number for the entire billing period. Major benefits to using IDR data are:
You do not know what your base load is with utility bill data. In other terms, you do not know how much energy your building is using when it is “turned off.” A quick way to save a lot of money is by reducing your base load- turning off lights, computers and air conditioning when leaving for the day, etc.
Can help you drive operational improvements like scoping out inefficiencies in consumption right when and where they occur. Real time data is very detailed, down to the exact meter and time of the day that the spike occurred. Also, ensuring proper equipment run time by comparing actual energy use to operating schedules and acting quickly to any inefficiencies.
Better manage demand by identifying the peak time of the day and how to decrease your consumption at these specific times. Read on to learn more about Peak Load Management
Goal Setting- “Interval data provides a more detailed understanding of operational patterns. In turn, this detail allows you to set more specific and meaningful goals and more accurately track your efforts toward attaining those goals” (urjanet).
Tenant/ Occupant Engagement- “The more specific recommendations you can provide occupants and the more closely you can tie those recommendations to desired outcomes, the more likely they will be to respond positively” (urjanet).
ENERGY STAR. Improve energy use in commercial buildings. Retrieved 2017, September 29, from https://www.energystar.gov/buildings/about-us/how-can-we-help-you/improve-building-and-plant-performance/improve-energy-use-commercial.
Katipamula, Koran, Taasevigen. U.S. Department of Energy. (2011, July). Interval Data Analysis with the Energy Charting and Metrics Tool (ECAM). Retrieved 2017, August 29, from http://buildingretuning.pnnl.gov/documents/pnnl_20495.pdf
Sun, Angela. Urjanet. (2016, October 27). 5 Use Cases for While Building Interval Data. Retrieved 2017, August 31, from https://aquicore.com/blog/real-time-data-vs-interval-data/.
September 20, 2017
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