ILLUME

Realizing Energy and Bill Savings for Small Businesses through Data

There are barriers to serving the small business sector, including the perception that it is difficult to achieve significant energy bill savings for these customers. However, retrocommissioning can often generate cost-effective and significant impacts for small business. In this data visualization, we share learnings from a remote retrocommissioning pilot targeting small businesses that ILLUME recently evaluated and illustrate how impactful these programs can be in addressing the needs of capital and resource-limited small businesses.

Small Retail Store

Re-Examining Opportunity

High-resolution data captured by advanced metering infrastructure (AMI) creates new opportunities to identify cost-effective energy savings opportunities for small businesses. For example, the utilization of AMI data enables remote retro-commissioning of buildings by detecting anomalies in energy consumption, thus pinpointing areas where a building may deviate from its intended design and fail to meet the owner’s operational needs.

Energy Use Signatures

Energy consumption data provides valuable insights into how a building is operating. For example, if we examine the annual historic energy usage of a small retail store, we can search for patterns in these data. Understanding these patterns will help us identify which businesses have an opportunity to save energy via enhancing building system operations and the magnitude of savings potential for small businesses.

Let's look at energy usage data for a small retail store.

Historic Energy Usage Data for a Small Retail Store

34.2

35.5

36.4

35.8

39.9

39.4

35.4

36.1

38.4

33.1

31

33

Peaks

This small retail store generally reaches peak energy consumption between 7:00 and 9:00 p.m. on weekdays, but the daily and weekly energy consumption is non-uniform.

Historic Energy Usage Data for a Small Retail Store

27.4

30.2

30

31.4

32

33.9

31.9

29.2

32.4

29.2

27

24.5

Valleys

The energy consumption of the small retail store consistently reaches its lowest daily point at 24 kWh during the period of 9:00 a.m. to 12:00 p.m. Considering the store’s operating hours, its energy use data indicates that energy is consumed at varying levels even when the business is closed. This signifies a potential opportunity for the store to make changes to building system operations and schedules to save energy.

Non-Routine Events

Anomalies in energy usage can serve as indicators and inputs for assessing the potential for energy savings. A non-routine event (NRE) refers to a change in building energy use that is not attributable to the impact of an efficiency intervention or changes to the baseline operation. As such, NREs can create uncertainty in both predicting and assessing energy savings and represent a risk in data-driven (or meter-based) programs, particularly in pay-for-performance offerings where reported savings are the basis for monetary transactions.

Let's see if there are any non-routine events in our data.

Historic Energy Usage Data for a Small Retail Store

NRE Detection and Adjustments

Accurate detecting and accounting for NREs are important, but the identification and proper accounting for NREs are not trivial, and uniform guidance is still lacking. Often, service providers and evaluators attempt to standardize a building’s energy load profile and from there detect and correlate NREs with information about what may have occurred within the building, sometimes gathering this additional information from customers.

Modeling Potential

Service providers use proprietary data-driven models, sometimes in combination with building system and operation information collected via customer engagement, to identify customized recommendations for customers to implement.

These recommendations often include low- and no-cost changes to building systems, such as lighting and HVAC schedules, control sequences, and set points. Some providers will also monitor participant energy usage after implementing recommendations on an ongoing basis to both verify the persistence of energy savings and to offer pay-for-performance services, where services are invoiced based on the estimated savings.

Actual Energy Usage Data for a Small Retail Store (Post-Intervention)

Optimized Energy usage

The green line shows new daily energy consumption for this small retail store. Notice that the adjustments the store implemented created uniformity in energy use and an overall reduction in energy consumption.

Predicted energy usage without intervention

Using the model's estimates, the white dashed line shows what the energy usage would have been without the adjustments the small retail store implemented.

Verifying Achieved Impacts

To evaluate the savings of this pilot, the ILLUME team used similar daily and hourly modeling approaches to those used by the service provider. To test the sensitivity of savings approaches, we compared the estimated realization rates between four energy models: (1) the service provider’s daily model; (2) the service provider’s hourly model; (3) CalTrack’s recommended daily model; (4) and CalTrack’s recommended hourly model for evaluating pay-for-performance programs.¹

Overall, our aggregate savings estimates across all participants in the pilot aligned with what was reported by the service provider, and we derived a realization rate of 99%. Finally, we conducted robustness tests to better understand the consistency and reliability of the results to inform future evaluations.

¹CalTRACK - CalTRACK Methods

Energy Savings

When model predictions are higher than the actual energy consumption, that's when savings happen – that gap is highlighted here in white. Check out how much the small retail store is saving each day!

Knowledge is power

Giving businesses this data engages and empowers them to make decisions. The small retail store from our pilot can inspect the data and further adjust their lighting and HVAC schedules to operate as efficiently as possible.

Imagine What a Small Business Could Save Overtime!

Scroll down to find out.

Imagine What a Small Business Could Save Overtime!

Customers who implement the recommendations derived from remote retro-commissioning can achieve cost savings by capitalizing on the energy efficiencies generated throughout their building systems. In one case study, ILLUME confirmed energy savings exceeding 600 kWh across 20 small business participants involved in the pilot program. Beyond the monetary benefits, these businesses recognized various advantages associated with participating in the pilot, such as the optimization of systems and building operations, access to expertise and support, and a more comprehensive understanding of their energy consumption patterns.

Key Considerations

Data-driven energy savings opportunities, like remote retro-commissioning, can create savings for small businesses as well as other benefits, including increased thermal comfort and an enhanced understanding of building operations and energy consumption.

But data-driven opportunities require data to work. More specifically, they have increasingly required historic and high-resolution, sub-integer, data. In many instances, these data are not collected and saved, so data availability might be a limiting factor in leveraging its power.

For example, some data-driven offerings require up to 12 months of interval data to enable their tools. Therefore, it could potentially require a period of up to 12 months following the resolution of any data issues before tools can properly leverage interval usage data.

It is also important to acknowledge and take into account that certain small businesses operate in communities where investments have not been made to establish AMI infrastructure or other comparable mechanisms for capturing and sharing high-resolution energy consumption data.

As we strive to expand and enhance equitable services within the energy industry, it is imperative to carefully consider how the incorporation of data-driven program offerings can effectively broaden our reach and serve the underserved small business customer segment.

Explore Volume 5 more

Big Data and Small Business

Using Data-Driven Models to Reveal Energy Savings Opportunities in an Underserved Customer Segment

Realizing Energy and Bill Savings for Small Businesses through Data

Small Retail Store

Re-Examining Opportunity

Energy Use Signatures

Let's look at energy usage data for a small retail store.

Historic Energy Usage Data for a Small Retail Store

Historic Energy Usage Data for a Small Retail Store

Peaks

Historic Energy Usage Data for a Small Retail Store

Valleys

Non-Routine Events

Let's see if there are any non-routine events in our data.

Historic Energy Usage Data for a Small Retail Store

NRE Detection and Adjustments

Modeling Potential

Actual Energy Usage Data for a Small Retail Store (Post-Intervention)

Optimized Energy usage

Predicted energy usage without intervention

Verifying Achieved Impacts

Energy Savings

Knowledge is power

Imagine What a Small Business Could Save Overtime!

Imagine What a Small Business Could Save Overtime!

Key Considerations