most citedImproving the Accuracy and Interpretability of Neural Networks for Wind Power Forecasting

3 citations · 5 across the 2 of their papers we have counts for

collaborators

2 papers

eess.SY2023★ 3 cited

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…

cs.LG2023★ 2 cited

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…