Study Reveals How Solar Homes Weather Blackouts Differently

Hoboken, N.J., Feb. 7, 2025 — Scientists at Stevens Institute of Technology have created a novel method for determining the homes that are most vulnerable to blackouts based on advanced AI technology to monitor power usage trends. The research comes at an opportune time with rising numbers of homes throughout the U.S. incorporating solar and all-electric systems as a means of countering climate change.

As the nation increasingly depends on electrified homes, and over a quarter of these already totally electric and solar installations set to triple in five years, it is now integral to emergency planning to know how such homes perform during extreme weather.

In the recent study, conducted by Philip Odonkor, a Stevens professor, researchers used AI techniques on DOE building-stock data to research the energy consumption of 129,000 single-family homes spread across eight states. The intent was to seek out the unique energy "signatures" of electricity-only homes versus those running on multiple types of energy and the appliances which have become electric.

The result showed consequential results up to vulnerability of solar and fully electric homes under different climatic conditions. While solar houses showed resistance to heatwaves in the summer season, they were gravely vulnerable to storms in the winter season. Electrically powered homes proved to be nearly three times more vulnerable to power outages in the winter season compared to their blended energy competitors.

This is one of the solar panel weaknesses that cannot meet enormous heating requirements during winter blackouts. The above findings are highly relevant against the recent backdrop like the February 2021 Texas winter storm when millions remained in darkness.

The researchers also developed machine-learning software that allowed them to identify homes' energy systems and vulnerabilities with over 95% accuracy, based on their power usage patterns. The technique can now be used by utilities and emergency responders to detect homes most likely to lose power without requiring door-to-door surveys or inspections, without intruding into homeowners' privacy.

This machine learning approach provides a cost-effective and scalable way of assessing vulnerabilities in communities, which may be crucial for emergency response agencies and urban planners. It's a more rapid and targeted way to address responses to severe weather catastrophes. This is especially critical as much of the area still suffers from overloads on old electric infrastructure and growing incidences of severe weather.

As the world moves to a future with more electrified households, resilience is clearly at the forefront. The research indicates that although the transition to clean power is most critical in terms of reducing environmental impact, equally so is paying attention to the way such systems operate when instances of extreme weather conditions, particularly winter crisis, arise.

Conclusion:In addition to informing emergency response, the research also has robust city planning implications. As cities produce new inventory of housing and strive to become more climate resilient, the vulnerabilities of solar and electric homes will be a primary consideration in building more resilient communities. The findings of the research indicate the importance of finding a balance between sustainability and ensuring stable power systems that are able to withstand extreme weather conditions. As climate events continue to increase globally, such data will be invaluable in designing green and resilient communities.