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Home > Impact > Funded Projects > Machine Learning Identification of Real-Time Wind Direction and Velocity from a Novel Anemometer Integrated into an Emission Mass Flowmeter
The project used a machine learning algorithm to optimize the computing process for a virtual anemometer intake system to estimate wind direction for the purposes of emission measurement, which is targeted for mobile and remote applications. This technology is proposed to be used in emissions measurement for greenhouse gas emission (GHG) reporting, specifically sources of non-continuous GHG emissions and/or varying atmospheric and environmental condition for various market applications include oil and gas, agriculture, manufacturing, landfills, commercial real estate, and more.