DNV’s Playbook for IRA Home Energy Rebate Program Success: Innovative Technology

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In the first two parts of DNV’s Playbook for IRA Home Energy Rebate Success, we emphasized that supporting and developing contractors along with designing equitable programs are crucial for the successful implementation of the Home Energy Rebate Programs. States, then, must look for ways to minimize overall administrative costs and maximize the value of these dollars to allocate the required funding to ensure successful participation. Strategically leveraging automation in four key independent project application workflows will maximize the customer benefit and reduce the time required for manual application review, ensuring targeted support where it is needed most. 

Home Energy Rebate applications are by their nature high-touch, due to the complexity of what must be validated and reviewed, no matter the program track. When a new project application arrives, this burden can be mitigated by using technology in novel ways to validate applicant eligibility through categorical income verification, understand the customer risk profile through a programmatic analysis of their savings potential, and automatically layer in other available rebate and tax incentive opportunities.

These automations are a boon to both the applicant seeking to maximize their rebate and the state looking to maximize the programs’ impacts even before the work begins. Finally, once the installation is complete, states should employ automated workflows and document data extraction capabilities to minimize the administrative overhead of processing invoices, shortening the time required to issue the final rebate.  

The following workflow automations bridge the gap between complex rebate processes and customer satisfaction, streamlining the journey from application to approval. By reducing manual intervention, they not only accelerate the validation and review stages but also enhance the overall experience for applicants, ensuring that Home Energy Rebate Programs are as user-friendly as they are effective.

1. Leverage AI & Data Sharing for Categorical Income Verification

Both the Home Efficiency Rebate Program (HER) and the Home Electrification and Appliance Rebate (HEAR) Program have a provision allowing for households already enrolled in specific social assistance programs to be automatically, or categorically, eligible. This eligibility will not cover all applicants, but the ability for a program to quickly determine eligibility in this way will mean reduced application friction and review. States should consider a multifaceted approach to this validation process that best supports the customer and the materials they may have on hand, namely documentation of existing enrollment in qualifying assistance programs. Using this documentation as a qualification source can be done with AI-driven documentation scanning that can ingest scanned or photographed program documents and extract eligibility features from the various documents of the more than 10 identified eligible programs. This criteria determination can be done without the need to anticipate the structure of every possible document. In addition to leveraging AI to extract eligibility from documents customers have on hand, states should look to potentially develop:  

• Data sharing agreements between sister agencies administering these co-eligible assistance programs. These agencies often have existing data pipelines for non-profit organizations supporting assistance implementations, which could enable quick lookups for some of the IRA’s listed programs. 
• Take advantage of newly developed or existing data pipelines with utilities to gain access to rate codes as an eligibility proxy. Many utilities have a unique rate code for approved social assistance enrollees, potentially removing the need to double qualify an applicant. 

2. Analyze Savings Potential & Understand Customer Risk

While a building performance model is only required for the modeled performance path of the Home Efficiency Rebate Program, we’d suggest an energy model for either the measured or modeled path to set savings expectations for a customer. Not only does this allow the state to initiate the proper application workflow, the submitted energy model gives states the ability to programmatically identify if the submitted path is best suited for the customer or if the benefit or risk of this versus another path is worth communicating to the customer through the contractor. Simplified example scenarios might look like the following:  

• A low-income customer with electric resistance heat who already significantly restricts their consumption may be projected to save 20% but won’t realize the full rebate potential relative to their project cost in the measured path due to their lower overall consumption. This customer could benefit from the flat rebate amounts of the modeled approach instead. 
• The projected savings in relation to project cost are both high enough to exceed the rebate cap on the modeled path and the measured path would maximize the customer rebate.  
• The occupant data and prior historical energy consumption submitted with the optional energy model suggest that the customer has already had or will undergo a non-routine event such as a natural disaster, changes in occupancy, or significant lifestyle changes otherwise not accounted for which may change their year over year measured energy consumption.

3. Leverage Other Funding Sources with Home Energy Rebates

To maximize customer benefit and potentially extend the impact of the IRA funds, states should establish a prioritized state-wide energy efficiency technology taxonomy and link it to their application workflows. This structured classification system would serve as a foundational tool for automatically comparing submitted applications against other incentives offered by local utilities or federal and state programs. By creating a unified taxonomy, states can ensure that rebates are extended and that homeowners are fully informed of all available incentives, maximizing the potential for energy savings and incentives. This taxonomy would also streamline the application process, reduce redundancy, and facilitate the integration of various programs, thereby accelerating the adoption of energy-efficient technologies and contributing to broader environmental and economic goals. A well-constructed taxonomy would act as a clear guide for customers, contractors, and stakeholders such as utilities, promoting transparency and encouraging even deeper participation in energy efficiency initiatives within the state.

4. Installation and Rebate Verification

In the pursuit of touchless application reviews, states should harness the power of automation within their application workflows for Home Energy Rebate Programs. By integrating their application portals with automated software solutions, states can ensure that each application is thoroughly checked for completeness. Automated systems can validate data within fields against predefined ranges or datatypes, ensuring accuracy and consistency. These systems can compare the values submitted in application data fields against those of the installed technologies provided in the accompanying invoices. This form to invoice validation utilizes the same AI-driven documentation scanning and data extraction capability configured for categorical income verification. These types of workflow automation not only enhance the precision of the review process but also significantly reduces the need for manual intervention, allowing for a higher volume of ‘no touch’ application reviews. Such a streamlined process not only expedites the approval of rebates but also minimizes errors, leading to a more efficient allocation of resources and a better experience for applicants.  

Technology has been a driving force behind the scale of utility demand side management programs across the country for years. By bringing these capabilities to bear on streamlining and accelerating the journey from application to approval, technology can similarly enable robust, scalable, and cost-effective implementation of the Home Rebate Programs.  

Let’s discuss: Putting these suggestions into practice is more challenging to accomplish than to discuss. DNV is here to help. Our Technology team brings 20+ years of experience developing software to deliver both consulting and software-as-a-service (SaaS) solutions enabling the successful implementation of rebate programs. DNV's recent acquisition of ANB Systems further solidifies our strength in this area. ANB Systems, a prominent SaaS company founded in 1997, brings DNV even more robust workflow tracking tools, digital TRMs, and AI-driven automation to reduce chances for human error and shorten application review and payment cycles. Connect with our team to discuss how we can help you design and implement the most impactful technological solutions as part of your IRA programs.