LF Energy has announced that the U.S. Department of Energy has approved the LF Energy OpenEEMeter as the first and currently only solution for measured pathway software verification under the Inflation Reduction Act (IRA) Home Efficiency Rebates (HOMES) Program. This program mandates the use of approved software solutions for calculating rebates based on utility consumption data before and after upgrades, with OpenEEMeter being the only one that meets these criteria. The software is open source, fitting within the requirements established by the IRA.

This approval designates OpenEEMeter as essential for the implementation of the IRA's measured savings programs nationwide. By standardizing calculation methods for measured energy consumption through open-source software, state energy offices can effectively deploy performance-based efficiency incentives, promoting transparency and accountability while also reducing the cost of energy-saving retrofits. This may ultimately lower carbon emissions and decrease energy bills for consumers.

The approval of OpenEEMeter reflects the project's capability in ensuring transparency and standardization within energy efficiency programs. The open-source nature of the software allows for public review and verification of measurement methods, drives community-focused enhancements, provides consistent application across various platforms, lowers barriers for new participants, and enhances the trust in the results of these programs.

Initially created by Recurve Analytics and contributed to LF Energy in 2019, OpenEEMeter has been developed through community collaboration into a comprehensive framework for assessing energy efficiency impacts. It is part of LF Energy OpenDSM, an open-source library aimed at measuring the impacts of demand-side programs utilizing historical data for model fitting and prediction comparisons against observed post-intervention energy usage. OpenDSM includes various modules, with DRmeter for demand response management and GRIDmeter for addressing model inaccuracies using data from non-participating customers.