Energy efficiency programs reduce U.S. electricity demand by hundreds of terawatt-hours each year, helping utilities serve growing load without building new generation. Utilities invest billions annually to lower customer energy use and reduce bills.1
These programs play a critical role in reliability and affordability. As load grows and capital costs rise, utilities are under increasing pressure to ensure those investments are not just impactful, but cost-effective. All too often, however, the connection between program savings and actual avoided system costs is unclear.
As electrification accelerates and demand-side resources take on a larger role in planning, utilities need to understand not just how much energy efficiency programs save, but when and where those savings reduce peak demand, defer infrastructure, and reduce capacity purchases.
As we’ve seen across multiple intervention types in this series (demand response and DERs), this level of visibility is not always easy to achieve.
The Program Visibility Gap
The majority of efficiency programs estimate savings using engineering models and deemed values. These methods provide a transparent and predictable way to design programs and support the flow of rebates and incentives.
Standard program reporting based on modeled or deemed savings does not translate directly into grid impacts that planners can use in distribution system planning, where the timing and location of load reductions matter most. When results are aggregated across programs, the combined system impact is difficult to interpret, making it harder for both planners and program administrators to understand where efficiency is delivering the greatest value.
Traditional evaluation functions largely as a compliance exercise, focusing on portfolio savings estimates from limited samples rather than showing how actual impacts vary across time, location, or customer types. Results often arrive on regulators’ desks years after projects are completed, leaving little opportunity for program implementers to use those insights to optimize programs in-flight.
The result is a visibility gap between program activity and grid outcomes. Utilities know how many projects were completed and what the estimated savings are, but they lack a way to see the impact on the grid.
The Consequences
When utilities cannot clearly see how efficiency affects system load (and ultimately how it affects ratepayers), they must rely on broad assumptions when incorporating programs into planning.
That uncertainty makes it harder to compare with supply-side investments or the cost of distribution-system upgrades, even when efficiency is the lower-cost option and quicker to deploy.
Aggregate program results can also mask significant variation in performance across participants, vendors, or technologies.
Recurve’s analysis of one utility’s weatherization programs found that the top 25 percent of participating customers achieved three times the savings of the remaining participants, with significantly stronger peak reductions during critical hours.
Without visibility into those underlying patterns, programs risk overlooking the customers and projects that deliver the greatest value and missing opportunities to increase their enrollment.
What It Takes to Fix Efficiency Programs
Improving the visibility of efficiency performance does not require changing how programs are designed or how incentives are paid. What utilities need is a complementary layer of analysis that connects program activity to real-world energy use, and the ability to translate program results into the same metrics used in planning, like peak demand and avoided cost.
Let’s apply our key principles to energy efficiency programs:
1. See how efficiency changes real-world energy use.
Meter data provides a direct view of how actual energy consumption changes after efficiency projects. Instead of relying solely on engineering assumptions, utilities can observe how buildings perform under real operating conditions, including the variability introduced by installation, operations, and occupant behavior.
Example: A regional pay-for-performance efficiency program measures savings directly from building energy use rather than relying solely on engineering estimates. This approach allows the program to support a wide range of retrofit projects and vendors while tying incentives to verified performance to ensure program impact aligns with affordability goals.
2. Learn which projects deliver the most impact.
When the feedback loop is shortened, and performance can be observed shortly after project completion, utilities can begin identifying which customers, buildings, and interventions deliver the strongest savings and peak demand reductions.
This becomes more important as electrification expands. Some projects may increase total energy use while still reducing system costs, depending on when that load occurs.
Instead of waiting years for evaluation results, teams gain earlier insight into program performance and can focus outreach and incentives on the segments delivering the greatest value.
This added visibility complements existing evaluation approaches. Whether programs rely on deemed savings, engineering estimates, or performance-based methods, seeing how projects affect system load helps utilities strengthen program outcomes.
Example: Recurve’s analysis of an electrification portfolio revealed significant differences in project outcomes. The top quartile of projects delivered more than $550 in annual bill savings, while the bottom quartile resulted in an average bill increase of ~$80. High-performing projects also produced three times the system benefits associated with peak demand reductions and 78% higher greenhouse gas savings.
By isolating the drivers behind these outcomes, program teams were able to redesign incentives and outreach to focus on the locations and projects delivering the greatest impact.
3. View efficiency performance across the portfolio.
When performance can be measured consistently across programs, utilities gain a clearer view of how demand-side resources affect system load. This broader visibility helps planners understand how efficiency interacts with electrification, load growth, and other demand-side interventions. It also makes it easier to incorporate efficiency into forecasting, resource planning, and regulatory discussions.
Example: A utility managing a large demand-side management portfolio worked with multiple vendors, each using different software platforms and savings estimation methods. To reconcile results across programs, the utility applied a common meter-based approach to measure avoided cost value, greenhouse gas reductions, and other key metrics across the portfolio. This provided a consistent view of how programs were affecting the grid beyond compliance reporting.
With clearer visibility, energy efficiency becomes easier to evaluate alongside other grid resources.
What Comes Next
Efficiency programs already deliver meaningful savings for customers and the grid. The goal is not to change how programs run, but to prove that they lower costs and support the grid.
When utilities can see how efficiency affects actual load, programs become an invaluable asset as utilities plan for load growth and contend with affordability issues.
Next up: The final article of this series will bring these ideas together and outline a practical action plan to make demand-side programs a more visible and dependable grid resource.
Connect with our team to learn how FLEX can support you and your team’s demand-side goals.