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How AI can help grids accommodate more solar PV

How AI can help grids accommodate more solar PV
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Balloon Juice - Risk Adjustment comment letter

Balloon Juice - Risk Adjustment comment letter
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Using deep neural networks to estimate impacts of work zones

Using deep neural networks to estimate impacts of work zones
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"Improving rock mechanical properties estimation using machine learning" by Ruizhi Zhong, Matt Tsang et al.

ABSTRACT: Rock mechanical properties (e.g., uniaxial compressive strength or UCS, Young’s modulus, and Poisson’s ratio) are important input parameters for geotechnical assessment and excavation designs. Two common methods used to obtain these parameters are laboratory testing and geophysical logging. The former delivers probably the most reliable results, but can be costly and time-consuming and for a lot of the time it is challenging to source sufficient samples. Alternative ways to better predict rock mechanical properties are needed.
In this case study, the XGBoost machine learning algorithm was applied to correlate laboratory and geophysical logging data with the three mechanaical properties of UCS, Young’s modulus, and Poisson’s ratio. The proposed machine learning approach better predicted UCS values with a smaller Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) and a larger R2. Similarly, better results were obtained for the Young’s modulus prediction us ....

Mean Absolute Error , Root Mean Square Error , சராசரி பிழை , வேர் சராசரி சதுரம் பிழை ,