The electrification of heat pumps and heating systems has emerged as a key strategy in the transition to net-zero. By moving away from fossil fuel-powered heating and cooling – which according to McKinsey research accounts for 2.2 billion tons of global CO2 emissions – the U.S. has the potential to reduce home heating emissions by up to 93%.
Policymakers have aptly recognized this potential. In September 2023, more than half of U.S. governors collectively committed to reaching 20 million residential electric heat pump installations by 2030. With roughly 4.8 million electric heat pumps installed across the country today, that means another 15 million must be deployed in the next seven years.
While most utilities already have targets in place to put a certain number of heat pumps in the field, there has been a massive conversion struggle because the economic story doesn’t line up for most consumers. New regulator funding (and sizable incentives from the Inflation Reduction Act) has been given to utilities to drive this adoption through rebates and tax credits that offset the upfront costs of an electric heat pump. But, just making heat pumps more affordable isn’t enough to scale fast enough by 2030.
The reality is most people are not even aware heat pump incentives are available. Of those who are, many still don’t understand the benefit of making a seemingly unnecessary appliance upgrade. So, instead of being seen as another company trying to sell a product, utilities need to become a trusted partner in the eyes of its customers in order to achieve an initiative of this magnitude.
Becoming a trusted partner starts by understanding each customer’s individual energy behaviors. This way, utilities can target the right customers, at the right time, with information most relevant to them. But with 15 million heat pumps to deploy, which customers will give utilities the best return on investment?
Identifying the “Right” Consumers
Layering AI-powered data analytics software onto existing smart meter data, utilities can build individual profiles for each home detailing its daily consumption down to the appliance. From these profiles, utilities can segment customers by a myriad of criteria, including appliance ownership (i.e. heat pump, pool pump, electric vehicle charger), times of use, and type of observed behavior – like higher-than-average air or heating usage.
This enables utilities to further segment customers based on average load consumption, location on the grid, or appliance duty cycle. From there, utilities can optimize engagement with each customer segment through more personalized messaging.
Homes using alternative fuel types for heating
AI analytics can even disaggregate heating and cooling appliance types to identify if the home has a central furnace, wood furnace, baseboard heater, mini-split, or packaged terminal air conditioner, for example. While this helps utilities easily segment separate homes that already have electric heat pumps from those that do not, it also identifies if the home is running off oil or gas-based fuel.
Homes with highest heating loads
One of the most effective subgroups to target is large load households, and this is who most utilities are already targeting today with mass marketing. However, to optimize outreach, utilities have the opportunity to use AI-driven data to learn each customer’s individual needs and preferences, then use those learnings to engage with them more strategically.
Customers who are motivated by sustainability, energy efficiency, savings, and comfort should all be messaged differently. An environmentally conscious customer will find carbon reduction impacts most compelling, while a savings-minded customer is likely to respond best to messages about incentives available to mitigate the costs associated with converting. Further, customers who have already embraced solar and/or electric vehicles are likely to be interested in how switching to a heat pump will further beneficially electrify their lives.
To the savings-minded customer, for example, the home’s “energy profile” enables utilities to share detailed and personalized scenarios that demonstrate the current costs of their home’s fossil fuel appliances versus the saving potential possible if they switched to electric appliances.
Homes with inefficient water heaters, HVAC systems, or gas appliances
This appliance level intelligence can be especially useful in identifying when to target customers. As HVAC devices, water heaters, and other appliances begin degrading, they start consuming more energy than usual. Through data analytics, utilities can run queries on changes in the duty-cycle curve by capturing how frequently an HVAC compressor switches on and off in order to regulate the home’s temperature. This inefficiency signals that it’s time for an appliance upgrade.
It’s important to note that customers who fall into high use and inefficient appliance categories are two of the greatest electric appliance targets because their payback period will be faster, even after initial rebate amounts.
Low-to-medium income households
Energy efficiency is especially important to low-to-medium income (LMI) households that are looking to keep electricity costs down. Unfortunately, LMI customers have long been an underserved segment as their bills and usage is generally smaller.
The good news, though, is that AI gives utilities the tools to better engage with this segment in a more meaningful way. For instance, if the historical usage data of a particular LMI household shows patterns of higher energy consumption and monthly expenses during winter months, AI-powered data insights can reveal an old heating system in the home.
The Power of Data Analytics
Mass marketing may convert a few hundred people, but scaling heat pump adoption among such a wide net of customers only happens when utilities speak to the individual needs and goals that matter the most to each customer. Harnessing data-driven household insights to support heat pump adoption empowers utilities to effectively connect with customers in a way that motivates them to take action.
Deciding which segment to target will depend on the utility’s own strategic objectives – whether that’s reducing load strain on a particular feeder, ramping up heat pump enrollment numbers across the board, or targeting low-to-medium income communities for upgrades. In general, however, identifying customers with the greatest propensity to adopt heat pumps and those customers who will receive (and deliver) the greatest benefit from adoption significantly increase a utility’s chance of success.
With smart meters being deployed in droves across the country, advanced AI analytics offers the key to transforming data into actionable customer intelligence. Now, in addition to optimizing marketing budgets, utilities can fulfill their regulatory requirements, achieve decarbonization goals and build stronger grid resilience by promoting heat pump adoption with greater success.