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BiLSTM Network-Based Approach for Solar Irradiance Forecasting in Continental Climate Zones

Author

Listed:
  • Mohammed A. Bou-Rabee

    (Department of Electrical Engineering, College of Technical Studies, PAAET, Safat 13092, Kuwait)

  • Muhammad Yasin Naz

    (Department of Physics, Plasma and Flow Assurance Lab., University of Agriculture, Faisalabad 38040, Pakistan)

  • Imad ED. Albalaa

    (Department of Science, College-Basic Education, PAAET, Safat 22081, Kuwait)

  • Shaharin Anwar Sulaiman

    (Department of Mechanical Engineering, Universiti Teknologi Petronas, Persiaran UTP, Seri Iskandar 32610, Perak, Malaysia)

Abstract

Recent research on solar irradiance forecasting has attracted considerable attention, as governments worldwide are displaying a keenness to harness green energy. The goal of this study is to build forecasting methods using deep learning (DL) approach to estimate daily solar irradiance in three sites in Kuwait over 12 years (2008–2020). Solar irradiance data are used to extract and understand the symmetrical hidden data pattern and correlations, which are then used to predict future solar irradiance. A DL model based on the attention mechanism applied to bidirectional long short-term memory (BiLSTM) is developed for accurate solar irradiation forecasting. The proposed model is designed for two different conditions (sunny and cloudy days) to ensure greater accuracy in different weather scenarios. Simulation results are presented which depict that the attention based BiLSTM model outperforms the other deep learning networks in the prediction analysis of solar irradiance. The attention based BiLSTM model was able to predict variations in solar irradiance over short intervals in continental climate zones (Kuwait) more efficiently with an RMSE of 4.24 and 20.95 for sunny and cloudy days, respectively.

Suggested Citation

  • Mohammed A. Bou-Rabee & Muhammad Yasin Naz & Imad ED. Albalaa & Shaharin Anwar Sulaiman, 2022. "BiLSTM Network-Based Approach for Solar Irradiance Forecasting in Continental Climate Zones," Energies, MDPI, vol. 15(6), pages 1-12, March.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:6:p:2226-:d:774209
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    4. Zian Lin & Yuanfa Ji & Xiyan Sun, 2023. "Advance Landslide Prediction and Warning Model Based on Stacking Fusion Algorithm," Mathematics, MDPI, vol. 11(13), pages 1-20, June.

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