Using AI to Tackle Energy Affordability

The standard electric company model relies on a predictable cycle where infrastructure expands to meet growing demand. Today, that cycle is challenged by a historic demand surge. Driven by the massive energy requirements of data centers and the rapid shift toward electrification, today's demand is rising at a rate that, when coupled with persistent inflation, has triggered a nationwide focus on affordability.

Funding has been allocated to support a variety of assistance programs, from direct bill subsidies like the Low Income Home Energy Assistance Program (LIHEAP) and arrearage management plans to weatherization rebates and time-of-use rate restructuring. But, according to the American Council for an Energy-Efficient Economy, only 13 percent of low-income households are on the receiving end of support.

Closing the gap between aid eligibility and actual enrollment means prioritizing technology that enables electric companies to identify and engage all income-qualified customers. By leveraging AI-driven analytics, electric companies can move beyond broad census data, transforming consumption patterns and appliance usage into clear indicators of energy vulnerability across an entire customer base.

Specifically, AI decodes the unique “fingerprint” of every home, allowing electric companies to not only detect customers with high energy burdens at the individual household level but also accurately match customers with relevant programs and messaging.

Identify Inefficient Applications

Aging heating and cooling systems and failing water heaters are the two biggest drivers of energy burden, making these homes ideal for targeted equipment replacement and electrification subsidies.

Distinguish Between Structural Inefficiencies and Behavioral Spikes

Homes with poor insulation are best served by weatherization upgrades, whereas homes with dramatic spikes are prime candidates for demand response programs.

Streamline Enrollment Via Behavioral Proxies

Because specific consumption signatures correlate with socioeconomic need, electric companies can auto-enroll customers in assistance programs and remove the administrative paperwork that often prevents households from receiving aid.

Identifying an income-qualified household is only half the battle; the other half is earning the trust required to help them. AI enables a shift from mass marketing to personalized coaching, so that rather than sending generic energy-saving tips that feel out of touch with a struggling family’s reality, electric companies can provide specific, timely insights. 

For example, a notification shouldn’t just say “save energy,” it should alert a customer that their HVAC unit is underperforming and provide a direct path to a pre-qualified repair grant.

Layering generative AI (GenAI) onto these data insights, electric companies can even tailor the timing, language, channel, and tone of individual customer outreach to ensure assistance offers arrive exactly when they are most relevant—such as immediately following a high-usage weather event. Because when communication is rooted in a customer’s actual consumption reality, the likelihood of both participation and program 
success increases.

The whitepaper, “Exceeding Affordability Goals With UtilityAI: Leveraging AI and Behind-the-Meter Data to Better Serve Income-Qualified Customers,” details how these data-driven strategies enable precise, high-impact support for those who need it most.