Work

Short-Term Photovoltaic Power Prediction Based on CEEMDAN and Hybrid Neural Networks

Year: 2024

Type: article

Source: IEEE Journal of Photovoltaics

DOI: 10.1109/jphotov.2024.3453651

Authors: Songmei Wu, Hui Guo, Xiaokang Zhang, Fei Wang

Institutions: Shanghai University, Shanghai Key Laboratory of Power Station Automation Technology

Language: English

Topic: Solar Radiation and Photovoltaics

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

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

Open Access status: closed

Funders: National Natural Science Foundation of China, Program of Shanghai Academic Research Leader

Awards: G2983315911, G808811832

OpenAlex ID: W4402351954

FWCI: 6.19

Cites: 41

Cited by: 42

Related to: 10