Wind Power Prediction Based on the Stacking Model of XGBoost and Random Forest-Reference-Cited by-同舟云学术

Wind Power Prediction Based on the Stacking Model of XGBoost and Random Forest

Published:2022-08-03 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE 11th Data Driven Control and Learning Systems Conference (DDCLS)
language:
Short-container-title:

Author:

Liu Wangjie¹,Jia Li¹

Affiliation:

1. Shanghai University,Shanghai Key Laboratory of Power Station Automation Technology,Department of Automation, College of Mechatronics Engineering and Automation,Shanghai,P. R. China,200444

Funder

National Natural Science Foundation of China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9858328/9858343/09858471.pdf?arnumber=9858471

Reference16 articles.

1. Short-Term Wind Power Generation Forecasting Based on the SVM-GM Approach

2. Superposition Graph Neural Network for offshore wind power prediction

3. A hybrid deep learning-based neural network for 24-h ahead wind power forecasting

4. An improved random forest model of short-term wind-power forecasting to enhance accuracy, efficiency, and robustness

5. Wind Power Prediction Using Ensemble Learning-Based Models

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Multi-node wind speed forecasting based on a novel dynamic spatial–temporal graph network;Energy;2023-12

2. Wind Speed Forecasting using ARMA and Boosted Regression Tree Methods: A Case Study;2023 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE);2023-09-24

3. Wind Energy Prediction: Artificial Intelligence Perspective;2023 6th International Conference on Engineering Technology and its Applications (IICETA);2023-07-15