Version 1
: Received: 9 January 2024 / Approved: 10 January 2024 / Online: 11 January 2024 (09:31:36 CET)
How to cite:
Alkhayat, G.; Hamid Hasan, S.; Mehmood, R. The Effect of Using Aerosol Variables on the Performance of Deep Learning-based GHI Forecasting Models. Preprints2024, 2024010880. https://doi.org/10.20944/preprints202401.0880.v1
Alkhayat, G.; Hamid Hasan, S.; Mehmood, R. The Effect of Using Aerosol Variables on the Performance of Deep Learning-based GHI Forecasting Models. Preprints 2024, 2024010880. https://doi.org/10.20944/preprints202401.0880.v1
Alkhayat, G.; Hamid Hasan, S.; Mehmood, R. The Effect of Using Aerosol Variables on the Performance of Deep Learning-based GHI Forecasting Models. Preprints2024, 2024010880. https://doi.org/10.20944/preprints202401.0880.v1
APA Style
Alkhayat, G., Hamid Hasan, S., & Mehmood, R. (2024). The Effect of Using Aerosol Variables on the Performance of Deep Learning-based GHI Forecasting Models. Preprints. https://doi.org/10.20944/preprints202401.0880.v1
Chicago/Turabian Style
Alkhayat, G., Syed Hamid Hasan and Rashid Mehmood. 2024 "The Effect of Using Aerosol Variables on the Performance of Deep Learning-based GHI Forecasting Models" Preprints. https://doi.org/10.20944/preprints202401.0880.v1
Abstract
Solar energy adoption worldwide has expanded exponentially due to a surge in international interest in producing clean energy and the declining cost of solar power plants and their technology. It is anticipated that by 2050, solar will have surpassed fossil fuels to become the primary source of energy. However, one of the main challenges associated with solar energy production is the instability of photovoltaic (PV) power generation because of weather changes. Short-term forecasting of the power output of photovoltaic systems is essential for efficient management of the power grid and energy markets. This paper aims to evaluate the ability of deep learning (DL) models to provide accurate forecasting of hourly global horizontal irradiance (GHI) using different sets of features, including weather and aerosol variables along with solar radiation components. The results show that the best forecast skills are achieved by the long short-term memory autoencoder (LSTM-AE) model.
Keywords
GHI forecasting; deep learning; LSTM; autoencoder; solar energy; aerosol
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
