Advancements in residential energy systems are poised to transform how homes manage energy, leading to greater efficiency, affordability, and grid resilience. We are seeing a rise in intelligent infrastructure that can automatically adjust to realtime energy needs—making the residential grid more efficient, reliable, and responsive to growing electrification.

As one example, Purdue has developed a patent-pending algorithm that improves how electric water heaters respond to demand-response signals by utilities. Rather than just rely on flow meters, the system estimates hot water usage using just one additional temperature sensor installed on the inlet water line. This enables highly accurate predictions of household usage at a lower cost.

By integrating this predictive algorithm, water heaters can shift energy use away from peak hours while maintaining user comfort.

Here is how this can help:

• Reduce energy costs for the homeowners.
• Demand-response deployment for water heater manufacturers and utilities.
• Lower overall stress on the electric grid.

Field testing at Purdue’s DC Nanogrid House, even under extreme cold conditions as low as ‑20 °C, showed usage estimates were within 95–98% accuracy in a month, validating the effectiveness of the approach. The university is now pursuing patent protection and inviting partners to engage in development and commercialization.