most citedAn Explainable Framework for Machine learning-Based Reactive Power Optimization of Distribution Network

2 citations · 2 across the 1 of their papers we have counts for

collaborators

5 papers

cs.LG2024

Zero and Few Shot Load Forecasting with Large Language Models

Wenlong Liao, Chengrui Zhang, Zhe Yang +4

Deep learning models have shown strong performance in load forecasting, but they generally require large amounts of data for model training before being applied to new scenarios, w…

cs.LG2024

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…

eess.SY2023

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…

eess.SY20232 cited

An Explainable Framework for Machine learning-Based Reactive Power Optimization of Distribution Network

Wenlong Liao, Benjamin Schäfer, Dalin Qin +3

To reduce the heavy computational burden of reactive power optimization of distribution networks, machine learning models are receiving increasing attention. However, most machine…

cs.LG2023

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…