Jorge Gonzalez-Cruz
University at Albany, NY·Engineering
Power grids are under growing strain from extreme weather and from electrification, as cars and building heating switch from fuel to electricity. Electricity demand now depends more strongly on local weather near the ground.
- Fields
Engineering
Earth & Environmental Science
Computer Science
- Topics
electric power systems
power grid
load forecasting
- Grants
- 1 known grant, through Jul 2027
What they work on
- Assess the commercial translation potential of a physics-based, AI-supported load forecasting platform through I-Corps customer discovery with utilities, independent system operators, and planners.
- Translate high-resolution atmospheric simulations into decision-ready electricity load forecasts and peak demand risk metrics that preserve realistic links between temperature, humidity, and demand.
- Simulate how electrification of heating, cooling, and transportation interacts with mean and extreme weather to shape peak demand, by modifying end-use load characteristics across historical and future weather scenarios.
More about the research
Utilities and grid operators rely on weather-based demand forecasts for daily operations and long-term planning. Most current tools, though, use coarse weather data or short forecast horizons. They miss rare but severe events, such as heat waves combined with high humidity, that drive peak demand.
Why it matters
Utilities, grid operators, and planners need accurate forecasts of peak electricity demand as extreme weather and electrification increase. Better estimates of rare, high-demand events could reduce the impact of outages and support more efficient infrastructure investment. This could improve grid reliability as weather grows less predictable.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Sara Eftekharnejad
Syracuse University, NY·Engineering·Early-career
Build a scalable framework to quantify interdependent power grid uncertainties from renewable generation and component outages, combining cascading failure modeling with machine learning and statistics for near-real-time analysis.
Zhaomiao Guo
University of Texas at Austin, TX·Engineering·Early-career
Develop network models of how decentralized transportation and power system stakeholders with incomplete information interact across space and time as electric vehicles and clean energy expand.
Ying Huang
North Dakota State University, ND·Engineering
Identify key risks to North Dakota's energy systems from extreme weather and cyber threats, and assess statewide research capacity and gaps, through capacity assessments and scenario development.
The school
University at Albany: 15 labs on PI Pages, $472M a year on research, 144 research doctorates a year.
Reach out
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