Enhancing Short-Term Solar Photovoltaic Power Forecasting Using a Hybrid Deep Learning Approach-Reference-Cited by-同舟云学术

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Enhancing Short-Term Solar Photovoltaic Power Forecasting Using a Hybrid Deep Learning Approach

Published:2024 Issue: Volume:12 Page:108928-108941
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Thipwangmek Nattha¹^ORCID,Suetrong Nopparuj¹,Taparugssanagorn Attaphongse²^ORCID,Tangparitkul Suparit³^ORCID,Promsuk Natthanan¹^ORCID

Affiliation:

1. Department of Computer Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai, Thailand

2. Department of ICT, School of Engineering and Technology, Asian Institute of Technology, Khlong Nueng, Pathum Thani, Thailand

3. Department of Mining and Petroleum Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai, Thailand

Funder

Electricity Generating Authority of Thailand (EGAT)-Chiang Mai University (CMU), Thailand, Academic Collaboration Project by EGAT and CMU

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/6287639/10380310/10630508.pdf?arnumber=10630508

Reference37 articles.

1. On the History and Future of 100% Renewable Energy Systems Research

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1. Multi-Timeframe Forecasting Using Deep Learning Models for Solar Energy Efficiency in Smart Agriculture;Energies;2024-08-29

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