Intelligent Solar Radiation Forecasting Using Recurrent Deep

Learning Models for Photovoltaic Energy Planning

Mona Ahmed Yassen1,2,* Mohamed Gamal Abdel-Fattah1,2 Islam Ismael3

Hossam El-Din Moustafa1,2

1 Department of Electronics and Communications Engineering, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt

2 Faculty of Artificial Intelligence, Horus University, Egypt

3 Department of Electrical Engineering, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt

Received: January 19, 2026 R ev ise d: March 13, 2026 A cce pt ed : May 10, 2026 ⋆ C orr es po nding author

ABSTRACT

Accurate solar radiation forecasting is essential for improving the reliability of photovoltaic energy generation and

supporting effective solar battery management, particularly because solar radiation is highly variable and depends on

nonlinear interactions among meteorological and temporal factors. Although conventional prediction methods can

provide useful estimates, they often struggle to capture the sequential behavior of solar radiation caused by daily

sunlight cycles, atmospheric variation, and changing weather conditions. Therefore, this study aims to develop and

evaluate deep learning models for predicting Solar_radiation using meteorological data collected from the HI-SEAS

weather station over four months, from September to December 2016, where the main input variables include

temperature, humidity, pressure, wind direction, wind speed, and time-related features. Five recurrent deep learning

models were implemented and compared, namely Recurrent Neural Network (RNN), Long Short-Term Memory

(LSTM), Gated Recurrent Unit (GRU), Bidirectional Long Short-Term Memory (BiLSTM), and Attention-LSTM.

Before model training, the dataset was preprocessed by handling missing values, checking temporal consistency,

arranging the observations chronologically, and applying Min–Max normalization to ensure stable learning. Model

performance was assessed using multiple regression metrics, including MSE, RMSE, MAE, MBE, correlation

coefficient, R2, RRMSE, NSE, and WI. The experimental results showed that BiLSTM achieved the best overall

forecasting performance, with an MSE of 0.0014, RMSE of 0.0379, MAE of 0.0182, MBE of 0.0039, correlation

coefficient of 0.9750, R2 of 0.9494, RRMSE of 0.3645, NSE of 0.9494, and WI of 0.9865. GRU and RNN also

produced competitive results, achieving RMSE values of 0.0381 and 0.0382 and R2 values of 0.9489 and 0.9486,

respectively, while Attention-LSTM showed comparatively lower performance with an RMSE of 0.0492 and R2 of

0.9149. These findings indicate that recurrent deep learning models are effective for learning nonlinear and temporal

patterns in solar radiation data, with BiLSTM providing the most accurate and reliable predictions. The proposed

forecasting framework can support photovoltaic energy planning and solar battery decision-making by estimating

future solar radiation levels and helping determine whether solar energy utilization will be feasible under expected

weather conditions.

Keywords: Solar radiation forecasting Deep learning BiLSTM Meteorological time series Photovoltaic energy

management