An XGBoost-SHAP Model for Energy Demand Prediction With Boruta–Lasso Feature Selection

Energy demand prediction is essential in ensuring national energy security, promoting high-quality economic development, advancing sustainable development, optimizing the energy structure, and achieving dual carbon goals. In recent years, machine learning (ML) algorithms have been extensively used i...

Celý popis

Podrobná bibliografie

Hlavní autoři:	Yiwen Wang, Weibin Cheng, Yuting Jin, Jifei Li, Yantian Yang, Shaobing Hu
Médium:	Artigo
Jazyk:	Inglês
Vydáno:	IEEE 2025-01-01
Edice:	IEEE Access
Témata:	Energy demand prediction interpretability feature selection Boruta-Lasso model XGBoost SHAP
On-line přístup:	https://ieeexplore.ieee.org/document/11098871/
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!