Work

Interpretable Wind Power Prediction: A Machine Learning Perspective Using Lightgbm and SHAP

Year: 2024

Type: conference-paper

Source: International Conference on Artificial Intelligence and Automation Control (AIAC)

DOI: 10.1109/aiac63745.2024.10899746

Authors: Zibo Yan, Liulei Zhang

Institution: Guangdong University of Technology

Language: English

Topic: Energy Load and Power Forecasting

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure, Climate action

Open Access status: closed

OpenAlex ID: W4408018727

FWCI: 3.224

Cites: 10

Cited by: 4

Related to: 10