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PNNL s NeuroMANCER Software Empowers Smaller Businesses with Cutting-Edge SciML Solutions
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PassiveLogic and PNNL Partner to Advance ML Tech for Building Autonomy
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Deep Learning Trains Buildings to Optimize Efficiency
In the United States, residential and commercial buildings account for nearly 40 percent of all energy consumption. About 40 percent of that energy use is from heating, ventilation, and air-conditioning (HVAC) systems.
An emerging efficiency method for buildings called model predictive control (MPC) could reduce HVAC energy use by up to 50 percent without impacting occupant comfort. But the method has been hampered by high costs associated primarily with software and computing.
Now, a team of scientists at Pacific Northwest National Laboratory (PNNL) has developed a new deep learning approach that uses building data and physics knowledge to train the MPC. The learning approach allows nonexperts to optimize control of a building’s energy systems without the need for additional computing power and proprietary software.
Project Haystack Organization Publishes Fall 2020 Issue of Connections Magazine
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The Project Haystack Organization (https://www.project-haystack.org), a collaborative community addressing the challenge of utilizing semantic modeling and tagging to streamline the interchange of data among different systems, devices, equipment and software applications, today announced the publication of the eighth issue of their Connections Magazine. The Project Haystack Connections Magazine Fall 2020 issue demonstrates how the Haystack Community has continued to further solidify the value brought by its widely adopted open-source methodology for semantic tagging in the built environment. RICHMOND, Va. (PRWEB) December 15, 2020 The Project Haystack Organization (https://www.project-haystack.org), a collaborative community addressing the challenge of utilizing semantic modeling and tagging to streamline the interchange