Research
Our central scientific hypothesis posits that the escalating impacts of climate change are exacerbating the energy burden and threatening the resilience of energy systems, particularly in underserved communities. We hypothesize that AI-driven digital twin technologies can effectively address these challenges through enhanced forecasting, improved energy system planning, optimized operations, and informed energy policy.
To rigorously explore this hypothesis, we have formulated the number of research questions, each directly linked to specific research tasks. See the diagram below for a general overview of how our research tasks, activities, and deliverables are linked.
Research Questions
- How are evolving local and regional climate patterns intensifying challenges within energy infrastructure and demand, especially in vulnerable communities?
- What are the most effective methods for integrating sociodemographic factors into energy planning considering heightened climate change risks?
- How can the integration of AI analytics with digital twin technology enhance our capacity to forecast, plan, and operate energy systems amid dynamic climate scenarios?
- What new community-centric models and methodologies can be developed to proactively address energy equity and resilience in the context of the changing climate?
- How can vulnerability assessments focused on climate change impacts inform the development of energy policies that are both community-focused and climate-resilient?
- What are the essential indicators for assessing the effectiveness of AI-driven digital twin technologies in improving resilience and addressing energy inequity in the face of climate change?
Explore each of the research questions in more detail by checking the task page for that question.