Improving the Accuracy and Interpretability of Neural Networks for Wind Power Forecasting

no code implementations • 25 Dec 2023 • Wenlong Liao, Fernando Porte-Agel, Jiannong Fang, Birgitte Bak-Jensen, Zhe Yang, Gonghao Zhang

Deep neural networks (DNNs) are receiving increasing attention in wind power forecasting due to their ability to effectively capture complex patterns in wind data.

Feature Engineering Feature Importance

Explainable Modeling for Wind Power Forecasting: A Glass-Box Approach with High Accuracy

no code implementations • 28 Oct 2023 • Wenlong Liao, Fernando Porte-Agel, Jiannong Fang, Birgitte Bak-Jensen, Guangchun Ruan, Zhe Yang

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.

Short-Term Power Prediction for Renewable Energy Using Hybrid Graph Convolutional Network and Long Short-Term Memory Approach

no code implementations • 15 Nov 2021 • Wenlong Liao, Birgitte Bak-Jensen, Jayakrishnan Radhakrishna Pillai, Zhe Yang, Kuangpu Liu

Accurate short-term solar and wind power predictions play an important role in the planning and operation of power systems.

A Review of Graph Neural Networks and Their Applications in Power Systems

1 code implementation • 25 Jan 2021 • Wenlong Liao, Birgitte Bak-Jensen, Jayakrishnan Radhakrishna Pillai, Yuelong Wang, Yusen Wang

The data in these tasks is typically represented in Euclidean domains.

Time Series Time Series Prediction