This article about interval utility data was originally posted at www.urjanet.com

Top 7 cases for when to use interval utility data

When tasked with the responsibility of understanding and reducing energy consumption and cost, the first place one might look is the data contained in monthly utility bills. While utility bill data can provide great value when thoroughly analyzed, it does have limitations related to granularity and depth of information. What it does not simply allow you to view is the amount of energy consumed at different times of the day.

Interval data - data collected at defined intervals from smart meters - is growing in popularity because it provides much more detailed information than monthly utility bills. Interval utility data delivers insights on the relation between time of day and energy demand. Interval data is typically collected every 15 minutes or hourly to provide a wealth of granular information to energy professionals. Enterprises find this information extremely valuable in instances such as demand response analytics, decisions around implementing alternative energy, energy efficiency insights, and peak shaving initiatives, among other use cases. Below, are Urjanet's top 7 use cases for interval utility data:

1. Load shaping and curbing peak demand

Large commercial organizations pay a demand charge based on the highest amount of energy they consume during a given day. This charge can sometimes be a very substantial amount on the bill. Interval data helps you identify the detailed patterns of 15- to 60-minute energy consumption which can be used to implement employee programs to curb usage during those times.

2. Reconciling data from submeter and main meter readings

If your organization has third party submeters installed, it's possible that the sum of the parts doesn't always equal the whole. What this means is that the aggregation of submeter data may not match what the main, building-level meter reads. The building's submeters could be over or underreporting energy consumption, resulting in faulty data. Whole-building interval data becomes useful in reconciling data from the two different sources.

3. Identifying opportunities to use alternate energy sources

Sometimes installing solar panels or energy storage devices can prove to be a good alternative if the peak tariffs are high during a certain time period and your organization also experiences high demand during those times. Interval data can shed light on whether or not this is the case for your organization, and perhaps turn the conversation towards implementing more cost-effective sources of energy.

4. Selecting the right tariffs to save money

Residential, commercial, and industrial buildings all have different load profiles. Because the energy needs vary across different organizations, utilities have distinct tariffs to charge differently by building type, demand levels, rate schedules, and other factors. Selecting the right tariffs for your building's particular load profile is one of the first cost-cutting measures you can take. For example, if your building's load profile displays low demand during on-peak hours, there may be opportunities to save money by switching to a time-of-use tariff.

5. Identifying optimal building operating hours

You might be able to save on energy costs by opening your facility either at an earlier or later time. For example, if a facility is on a time-of-use tariff, and it opens at 8:00 AM, the facility will experience a surge in kWh consumed due to air conditioning, heating units, and other appliances and devices being turned on. If the facility is on a time-of-use tariff, and the utility's on-peak hours also begin at 8:00 AM, it may be worth shifting the facility's start time to an off-peak demand rate.

6. Validating demand response event actions

Many utilities offer demand response programs that offer cash payments and other incentives to participants that are able to shed load during periods of peak demand. Demand response vendors may sometimes need interval data to validate that there were indeed energy-reducing actions taken following a demand response event.

7. Equipment-level energy auditing

There may be opportunities to determine which particular assets in a facility drive the most energy consumption during certain hours of the day, without the need to install sensors on each asset. However, it may only be viable for smaller facilities with fewer and larger appliances to pinpoint and determine which pieces of equipment are responsible for energy consumption spikes.

These are only a few of the many use cases for interval data. Analyzing data from monthly utility bills is a good start in energy management, but a deeper dive with interval data may be a good next step to truly understand your building's energy consumption patterns.