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Full-Text Articles in Physical Sciences and Mathematics
Community Responses To Us Regional Clean Hydrogen Hubs, Bethani Turley
Community Responses To Us Regional Clean Hydrogen Hubs, Bethani Turley
Student Research Symposium
In 2023, the Bipartisan Infrastructure Law designated 7 billion dollars to fund regional hydrogen hubs across the US with the goal of kickstarting a utility scale hydrogen economy for the US electric grid. A promising technology in the renewable energy transition, hydrogen can be made from a multitude of energy sources, often designated by colors: green hydrogen is made from solar and wind, pink hydrogen from nuclear, and blue hydrogen from natural gas. This presentation examines this new hydrogen economy through the case study of the Appalachian Regional Clean Hydrogen Hub (ARCH2). ARCH2 is a blue hydrogen hub proposal by …
Machine Learning Predictions Of Electricity Capacity, Marcus Harris, Elizabeth Kirby, Ameeta Agrawal, Rhitabrat Pokharel, Francis Puyleart, Martin Zwick
Machine Learning Predictions Of Electricity Capacity, Marcus Harris, Elizabeth Kirby, Ameeta Agrawal, Rhitabrat Pokharel, Francis Puyleart, Martin Zwick
Systems Science Faculty Publications and Presentations
This research applies machine learning methods to build predictive models of Net Load Imbalance for the Resource Sufficiency Flexible Ramping Requirement in the Western Energy Imbalance Market. Several methods are used in this research, including Reconstructability Analysis, developed in the systems community, and more well-known methods such as Bayesian Networks, Support Vector Regression, and Neural Networks. The aims of the research are to identify predictive variables and obtain a new stand-alone model that improves prediction accuracy and reduces the INC (ability to increase generation) and DEC (ability to decrease generation) Resource Sufficiency Requirements for Western Energy Imbalance Market participants. This …
Data From: Machine Learning Predictions Of Electricity Capacity, Marcus Harris, Elizabeth Kirby, Ameeta Agrawal, Rhitabrat Pokharel, Francis Puyleart, Martin Zwick
Data From: Machine Learning Predictions Of Electricity Capacity, Marcus Harris, Elizabeth Kirby, Ameeta Agrawal, Rhitabrat Pokharel, Francis Puyleart, Martin Zwick
Systems Science Faculty Datasets
This research applies machine learning methods to build predictive models of Net Load Imbalance for the Resource Sufficiency Flexible Ramping Requirement in the Western Energy Imbalance Market. Several methods are used in this research, including Reconstructability Analysis, developed in the systems community, and more well-known methods such as Bayesian Networks, Support Vector Regression, and Neural Networks. The aims of the research are to identify predictive variables and obtain a new stand-alone model that improves prediction accuracy and reduces the INC (ability to increase generation) and DEC (ability to decrease generation) Resource Sufficiency Requirements for Western Energy Imbalance Market participants. This …
Climate Change Curricula In Alberta, Canada: An Intersectional Framing Analysis, Greg Lowan-Trudeau
Climate Change Curricula In Alberta, Canada: An Intersectional Framing Analysis, Greg Lowan-Trudeau
Northwest Journal of Teacher Education
This article is comprised of a climate change-focused framing analysis of proposed revisions to Alberta, Canada’s K-6 curriculum as an ideologically motivated manifestation of curricular epistemicide. Eisner’s three curricula—the explicit, implicit, and null—and scholarship related to intersectional climate and environmental justice, education, and communication provide the theoretical framework. This inquiry concludes with a critical discussion of and possible alternatives to the revised curriculum with further consideration of the implications for those involved with similar endeavours in other jurisdictions across Canada and around the world.
"Energy Resources", Harrison Scott Brown
"Energy Resources", Harrison Scott Brown
Special Collections: Oregon Public Speakers
With: Salter, C. L. The urban enigma.