Skip to main content

ORIGINAL RESEARCH article

Front. Energy Res.
Sec. Sustainable Energy Systems
Volume 12 - 2024 | doi: 10.3389/fenrg.2024.1485690
This article is part of the Research Topic AI for Renewable Energy Resilience View all articles

Leveraging Advanced AI Algorithms with Transformer-Infused Recurrent Neural Networks to Optimize Solar Irradiance Forecasting

Provisionally accepted
Muhammad Sabir Naveed Muhammad Sabir Naveed 1,2Muhammad Farhan Hanif Muhammad Farhan Hanif 1,2*Mohamed Metwaly Mohamed Metwaly 3,4Imran Iqbal Imran Iqbal 5Ehtisham Iodhi Ehtisham Iodhi 6Xiangtao Liu Xiangtao Liu 1,2Jianchun Mi Jianchun Mi 1,2*
  • 1 Peking University, Beijing, Beijing Municipality, China
  • 2 Department of Energy & Resource Engineering, College of Engineering, Peking University, Beijing, China
  • 3 King Saud University, Riyadh, Riyadh, Saudi Arabia
  • 4 College of Tourism and Archeology, King Saud University, Riyadh, Saudi Arabia
  • 5 Department Landschaftsökologie, Helmholtz-Zentrum für Umweltforschung UFZ, Leipzig, Lower Saxony, Germany
  • 6 Zhejiang University, Hangzhou, Zhejiang Province, China

The final, formatted version of the article will be published soon.

    Solar energy (SE) is vital for renewable energy generation, but its natural fluctuations present difficulties in maintaining grid stability and planning. Accurate forecasting of solar irradiance (SI) is essential to address these challenges. The current research presents an innovative forecasting approach named as Transformer-Infused Recurrent Neural Network (TIR) model. This model integrates a Bi-Directional Long Short-Term Memory (BiLSTM) network for encoding and a Gated Recurrent Unit (GRU) network for decoding, incorporating attention mechanisms and positional encoding. This model is proposed to enhance SI forecasting accuracy by effectively utilizing metrological weather data, handling overfitting, and managing data outliers and data complexity. To evaluate the model's performance, a comprehensive comparative analysis is conducted, involving five algorithms: Artificial Neural Network (ANN), BiLSTM, GRU, hybrid BiLSTM-GRU, and Transformer models. The findings indicate that employing the TIR model leads to superior accuracy in the analyzed area, achieving R 2 value of 0.9983, RMSE of 0.0140, and MAE of 0.0092. This performance surpasses those of the alternative models studied. The integration of BiLSTM and GRU algorithms with the attention mechanism and positional encoding has been optimized to enhance the forecasting of SI. This approach mitigates computational dependencies and minimizes the error terms within the model.

    Keywords: Transformer model1, Bidirectional LSTM model2, GRU model3, Deep Learning4, Solar irradiance5, Solar forecasting6

    Received: 24 Aug 2024; Accepted: 25 Sep 2024.

    Copyright: © 2024 Naveed, Hanif, Metwaly, Iqbal, Iodhi, Liu and Mi. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Muhammad Farhan Hanif, Department of Energy & Resource Engineering, College of Engineering, Peking University, Beijing, China
    Jianchun Mi, Peking University, Beijing, 100871, Beijing Municipality, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.