Work

A hydrid LSTM-XGBoost model for short-term photovoltaic power forecasting under complex weather conditions

Year: 2026

Type: article

Source: Indoor and Built Environment

DOI: 10.1177/1420326x261444028

Authors: Gaopeng Li, Li Bai, Sai Liu, Xinshuai Geng

Institutions: Jilin Jianzhu University, Southeast University, Université de Sherbrooke

Language: English

Topic: Solar Radiation and Photovoltaics

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

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

Open Access status: closed

OpenAlex ID: W7155165679

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Cites: 38

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