108 citations · 255 across the 5 of their papers we have counts for
5 papers
TimeGPT in Load Forecasting: A Large Time Series Model Perspective
Wenlong Liao, Fernando Porte-Agel, Jiannong Fang +4
Machine learning models have made significant progress in load forecasting, but their forecast accuracy is limited in cases where historical load data is scarce. Inspired by the ou…
Improving the Accuracy and Interpretability of Neural Networks for Wind Power Forecasting
Wenlong Liao, Fernando Porte-Agel, Jiannong Fang +3
Deep neural networks (DNNs) are receiving increasing attention in wind power forecasting due to their ability to effectively capture complex patterns in wind data. However, their f…
Explainable Modeling for Wind Power Forecasting: A Glass-Box Approach with High Accuracy
Wenlong Liao, Fernando Porte-Agel, Jiannong Fang +3
Machine learning models (e.g., neural networks) achieve high accuracy in wind power forecasting, but they are usually regarded as black boxes that lack interpretability. To address…
Revisiting the Local Scaling Hypothesis in Stably Stratified Atmospheric Boundary Layer Turbulence: an Integration of Field and Laboratory Measurements with Large-eddy Simulations
Sukanta Basu, Fernando Porté-Agel, Efi Foufoula-Georgiou +2
The `local scaling' hypothesis, first introduced by Nieuwstadt two decades ago, describes the turbulence structure of stable boundary layers in a very succinct way and is an integr…
Synthetic Turbulence, Fractal Interpolation and Large-Eddy Simulation
Sukanta Basu, Efi Foufoula-Georgiou, Fernando Porté-Agel
Fractal Interpolation has been proposed in the literature as an efficient way to construct closure models for the numerical solution of coarse-grained Navier-Stokes equations. It i…