Filter News
Area of Research
- (-) Computer Science (3)
- Advanced Manufacturing (5)
- Biology and Environment (105)
- Biology and Soft Matter (1)
- Building Technologies (2)
- Clean Energy (112)
- Climate and Environmental Systems (5)
- Computational Engineering (1)
- Electricity and Smart Grid (1)
- Energy Sciences (1)
- Functional Materials for Energy (1)
- Fusion and Fission (4)
- Fusion Energy (2)
- Isotopes (1)
- Materials (27)
- Materials for Computing (5)
- Mathematics (1)
- National Security (8)
- Neutron Science (10)
- Nuclear Science and Technology (2)
- Quantum information Science (1)
- Supercomputing (28)
News Type
News Topics
Media Contacts
![An algorithm developed and field-tested by ORNL researchers uses machine learning to maintain homeowners’ preferred temperatures year-round while minimizing energy costs. Credit: ORNL, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2021-07/2019-P07408_2.jpg?h=8f9cfe54&itok=jBvKdqIv)
Oak Ridge National Laboratory researchers designed and field-tested an algorithm that could help homeowners maintain comfortable temperatures year-round while minimizing utility costs.
![Smart Neighborhood homes](/sites/default/files/styles/list_page_thumbnail/public/2020-01/04.09.TD-SMartHome_0.jpg?h=5b5a5437&itok=22S5Tle1)
To better determine the potential energy cost savings among connected homes, researchers at Oak Ridge National Laboratory developed a computer simulation to more accurately compare energy use on similar weather days.
![Heat impact map](/sites/default/files/styles/list_page_thumbnail/public/2019-07/Winter_HDD_Change_ORNL.gif?h=e87b941e&itok=8t83D_u_)
A detailed study by Oak Ridge National Laboratory estimated how much more—or less—energy United States residents might consume by 2050 relative to predicted shifts in seasonal weather patterns