The Simple Building Calculator, developed at PNNL, meets a need for a quick, interactive, and economic method to evaluate energy use—and potential savings from efficiency measures—in simple commercial buildings.

Data-driven experiments can help determine best practices for Random Forest machine learning to predict water quality, particularly nitrate levels.

PNNL joint appointee Auroop Ganguly uses data science to study the impacts of climate change.

Across the United States, water moving between the river and riverbed sediments does not overcome localized processes that govern organic matter chemistry.

Eight Office of Science Graduate Student Research join PNNL.

New research simulates power demand, supply, and cost in a hotter, drier climate.

New work developed a methodology to generate building temperature setpoint schedules for use by modelers in large-scale building energy simulations.