An Innovative Behavioral Program Designed to Deliver Deep Energy Savings in the Residential Sector
Res-Intel has released Energize!, its energy efficiency (EE) program that utilizes data-driven outreach and best-in-class behavioral strategies to save energy in the residential building sector. Energize! utilizes Res-Intel’s mass-scale building energy benchmarking methodology to provide participants with a 1-100 grade on the energy performance of their homes. Community-based social marketing is used to foster participation in home energy retrofit competitions. Each competition is designed by the local community and awards prizes to the participants that achieve the most energy savings. This unique combination of information and motivation is designed to dramatically increase program participation and deliver energy deep savings.
Investment in residential solar photovoltaics has seen exponential growth in recent years while investment in EE, typically a more cost-effective option, has not. Energize! aims to increase investment in EE by replicating some of the most effective practices used in the solar photovoltaics market. These practices include utilizing marketing by service providers and utility trade allies, providing innovative financing models such as on-bill finance and green loans, as well as the use of advanced metered-energy savings methodologies. Energize! is designed to be implemented by innovative municipalities and utilities that are interested in improving social justice, energy, and economic outcomes in their jurisdictions.
Res-Intel recently presented Energize! at the California Local Government Commission’s 10th Annual Statewide Energy Efficiency Forum in Long Beach, CA. To request more information on the Energize! program, please click here.
Res-Intel is a national leader in mass-scale benchmarking of residential buildings. Its benchmarking engine provides micro-targeted recommendations for property owners and managers to cost-effectively reduce energy and water usage. The Res-Intel software platform has been partially funded under California Energy Commission Public Interest Energy Research grant #57356A/11-12 and #58076A/14-09G.
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