The Community Earth atmospheric Data System (CEDS) provides historical surface flux data that are used for general analysis and also for model validation through comparisons with observations.

PNNL’s pioneering CETC project with regional universities demonstrates transactive controls among multiple commercial buildings and devices for energy efficiency and grid reliability.

PNNL and collaborators have established a national heat pump and heat pump water heater partnership to help drive adoption of these energy-saving technologies in both residential and commercial buildings.

GeoBOSS is a software library that combines the data-handling capabilities of Spark and the user-friendliness of Python to simplify geospatial analytics and the transition between small-scale research and large-scale operational projects.

GODEEEP is an internal PNNL Agile investment inspired that addresses U.S. priorities related to clean energy and environmental and energy equity.

IrrigationViz is a visual decision-support tool that provides users with high-level estimates for irrigation modernization projects, such as concrete lining for a canal or replacing a canal with a pipeline.

The Joint Global Change Research Institute conducts research to advance fundamental understanding of human and Earth systems and provide decision-relevant information for management of emerging global risks and opportunities.

FEMP's operations and maintenance (O&M) resources offer federal agencies technology- and management-focused guidance to improve energy and water efficiency and ensure safer and more reliable operations.

Physics-informed machine learning (PIML) is a modeling approach that harnesses the power of machine learning and big data to improve the understanding of coupled, dynamic systems.

The user-friendly Project Schedule Visualizer software developed at PNNL helps users readily identify and understand the impacts of updates to the schedule, budget, and risks associated with large, complex projects that cross departments.

PNNL combines AI and cloud computing with damage assessment tools to predict the path of wildfires and quickly evaluate the impact of natural disasters, giving first responders an upper hand.

PNNL is working on behalf of the U.S. Department of Energy to create a prototype system that enables homes to help provide services to the power grid while delivering economic benefits to residents.

Powered by few-shot learning, the Sharkzor AI-driven, scalable web application makes it possible to quickly characterize and sort electron microscopy images used to analyze radioactive materials.

PNNL has developed a tool suite of interactive analytics that can be rapidly integrated into analyst workflows to empirically analyze and gain qualitative understanding of AI model performance jointly across dimensions.

PNNL creates immersive software experiences to meet a variety of challenges. One such challenge in science, technology, engineering, and mathematics (STEM) education is providing quality computer science education for all students.