Accurate daily solar radiation prediction is a crucial task for the management and generation
of solar energy as one of the alternatives to fossil fuels. In this study, the prediction accuracy …

The use and importance of renewable energy sources (RES) have been increasing every
passing year as fossil fuels will soon be depleted. Within this context, solar-photovoltaic (PV) …

The study aims to reveal which mother wavelet type performs best in evaporation prediction.
This study used a hybrid algorithm that combined K-Nearest Neighbors (KNN), Extreme …

This study models solar irradiance at six stations in Iran and the USA on an hourly scale. We
explored two seasonal emotional artificial neural networks (EANN): sequence-EANN …

This study investigates the estimation of daily solar radiation (SR) through various machine
learning (ML) models, including the k-nearest neighbor algorithm (KNN), support vector …

The data-driven method is a common approach to solar irradiation prediction. However, its
weakness is that it is highly dependent on the characteristics of the prior data, which leads to …

Hybrid deep learning model with multi-frequency capabilities is presented for simulating
solar irradiation. Utilizing hourly recorded solar irradiation and climate data, model employs …

The accurate prediction of hourly global solar radiation is critical to solar energy conversion
systems selecting appropriate provinces, and even future investment policies. With this …

Accurately predicting proton flux in the space radiation environment is crucial for satellite in-
orbit management and space science research. This paper proposes a proton flux prediction …

IIn recent years, the interest in renewable energy sources has increased due to
environmental damage and, the increasing costs of fossil fuel resources, whose current …