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What is Genset Noise Prediction via Machine Learning about?

This paper presents a machine learning-based tool for predicting sound power levels of gensets, utilizing regression algorithms such as Kernel Ridge Regression, Huber Regressor, and Gaussian Process Regression. The study leverages high-fidelity datasets from over 100 experiments to provide accurate noise level estimations during the initial design and bidding stages, achieving an average accuracy of ±5 dBA with KRR. The findings suggest that this approach can significantly aid in early-stage noise estimatio

Author

Saurabh Pargal

Language

EN