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NREL Unveils Groundbreaking Generative Machine Learning Model To Simulate Future Energy-Climate Impacts

NREL Unveils Groundbreaking Generative Machine Learning Model To Simulate Future Energy-Climate Impacts
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United statesPuerto ricoDan bilelloRyan kingAndrew glawsBrandon bentonJoe delneroMadeline geocarisJustin daughertyBilly robertsLos angelesNature energyWind integration national dataset toolkitRenewable energy studyResolution for renewable energy resource dataAmazon web services

New open-source generative machine learning model simulates future energy-climate impacts

New open-source generative machine learning model simulates future energy-climate impacts
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Puerto ricoUnited statesDan bilelloBrandon bentonJoe delneroRyan kingAndrew glawsLos angelesAmazon web servicesStrategic energy analysis centerComputational science centerResolution for renewable energy resource dataWind integration national dataset toolkitRenewable energy studyRenewable energyNational solar radiation database

Before You Install Wind Energy Technology, Check Out This Database

Before You Install Wind Energy Technology, Check Out This Database
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Great lakesCanada generalUnited statesPuerto ricoGulf of mexicoMexico generalNicholas gilroyCaroline draxlNational offshore windData sponsorship programAmazon web servicesNational renewable energy laboratoryUnited states agency for international developmentUs department of energyGroup highly scalable digital serviceWind integration national dataset toolkit

How Turbulent Winds Can Increase Efficiency in Concentrating Solar Power

How Turbulent Winds Can Increase Efficiency in Concentrating Solar Power
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United statesSalt lake cityStanford universityUniversity of utahBrooke stanislawskiStephanie redfernWerner slocumUlrike egererJulie lundquistDresden university of technologyUniversity of colorado boulderPennsylvania state universitySiemens energyUniversity of texas at austinDepartment of energyNorthwestern university