A radiant shift: Attention-embedded CNNs for accurate solar irradiance forecasting and prediction from sky images-Reference-Cited by-同舟云学术

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A radiant shift: Attention-embedded CNNs for accurate solar irradiance forecasting and prediction from sky images

Published:2024-11 Issue: Volume:234 Page:121133
ISSN:0960-1481
Container-title:Renewable Energy
language:en
Short-container-title:Renewable Energy

Author:

Jonathan Anto Leoba,Cai Dongsheng^ORCID,Ukwuoma Chiagoziem C.^ORCID,Nkou Nkou Joseph Junior,Huang Qi,Bamisile Olusola^ORCID

Publisher

Elsevier BV

Reference53 articles.

1. Enhancing hydrogen production prediction from biomass gasification via data augmentation and explainable AI: a comparative analysis;Ukwuoma;Int. J. Hydrogen Energy,2024

2. A Transformer-based multimodal-learning framework using sky images for ultra-short-term solar irradiance forecasting;Liu;Appl. Energy,2023

3. Solar energy technology and its roles in sustainable development;Maka;Clean Energy,2022

4. Recent advances in intra-hour solar forecasting: a review of ground-based sky image methods;Lin;Int. J. Forecast.,2023

5. A comprehensive review of grid-connected solar photovoltaic system: architecture, control, and ancillary services;Morey;Renewable Energy Focus,2023

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