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Water Energy Planning Strategies to meet Emerging Load from Data Centers
This white paper discusses the urgent need for innovative management of energy and water resources in the context of rapidly growing data centers, particularly as U.S. electricity demand is projected to increase by 35 to 40% by 2040 due to the rise of artificial intelligence (AI)....
Bhowmik, P., Cafferty, K., Klise, K., and Jackson, N.
Maximizing American Energy Dominance with Forecasts of Energy Infrastructure Flood Exposure and AI Predictions of Consequences
This white paper focuses on water for energy.
High frequency, reliable, and spatially explicit flood forecasts are necessary to forecast impacts to the energy system from hydrological hazards. While these tools are nearing operation at scale, they have not yet been systematicall...
Brelsford, C., Garcia, M., Robbins, Z., Schwenk, J., and Liu, Y.
Agentic Optimization for Resilience in Hydropower Reservoir Systems
The growing electricity demand in the U.S. requires a resilient energy strategy. Hydropower is a major component of that strategy but is subject to environmental and human stressors that impact its dependability. AI-driven reinforcement learning, called "Agentic" learning, presen...
Schwenk, J., Garcia-Cardona, C., Bennett, K., Singh, S., and Brelsford, C.
Employing AI-Enabled Prognostics for Energy-Water Resilience
This white paper focuses on strengthening the resilience and efficiency of hydropower systems at the energy-water nexus through AI-enabled prognostics and hardware-in-the-loop (HIL) simulation. It emphasizes integrating digital intelligence with physical infrastructure to optimize...
Zhao, S., Qiu, F., and Agalgaonkar, Y.
Hydrotwin: an AI-based physics and optimization framework for improved water resource resilience in systems experiencing adverse events
This white paper focuses on water for energy.
As we move ahead into an uncertain future for water resource demand and supply, a massive challenge exists to optimize water systems to be resilient to a range of press/pulse extreme event states and conditions. Optimal decision-makin...
Bennett, K., Schwenk, J., and Garcia, M.