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Solar Energy Production Forecasting Based on a Hybrid CNN-LSTM-Transformer Model
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Open AccessArticle
Solar Energy Production Forecasting Based on a Hybrid CNN-LSTM-Transformer Model
by Elham M Al-Ali 1,*,Yassine Hajji 2,Yahia Said 3,4ORCID,Manel Hleili 1,Amal M Alanzi 1,Ali H Laatar 5 andMohamed Atri 6ORCID
1
Mathematics Department, College of Sciences, Tabuk University, Tabuk 71491, Saudi Arabia
2
Laboratory of Energetics and Thermal and Mass Transfer (LR01ES07), Faculty of Sciences of Tunis, University of Tunis El Manar, Tunis 1068, Tunisia
3
Remote Sensing Unit, College of Engineering, Northern Border University, Arar 91431, Saudi Arabia
4
Laboratory of Electronics and Microelectronics (LR99ES30), University of Monastir, Monastir 5019, Tunisia
5
Physics Department, College of Sciences, Tabuk University, Tabuk 71491, Saudi Arabia
6
College of Computer Sciences, King Khalid University, Abha 62529, Saudi Arabia
*
Author to whom correspondence should be addressed
Mathematics 2023, 11(3), 676 https://doiorg/103390/math11030676
Submission received: 11 January 2023 / Revised: 23 January 2023 / Accepted: 27 January 2023 / Published: 28 January 2023
(This article belongs to the Special Issue Artificial Intelligence in Geoenvironmental and Energy Sciences)
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Abstract
Green energy is very important for developing new cities with high energy consumption, in addition to helping environment preservation Integrating solar energy into a grid is very challenging and requires precise forecasting of energy production Recent advances in Artificial Intelligence have been very promising Particularly, Deep Learning technologies have achieved great results in short-term time-series forecasting Thus, it is very suitable to use these techniques for solar energy production forecasting In this work, a combination of a Convolutional Neural Network (CNN), a Long Short-Term Memory (LSTM) network, and a Transformer was used for solar energy production forecasting Besides, a clustering technique was applied for the correlation analysis of the input data Relevant features in the historical data were selected using a self-organizing map The hybrid CNN-LSTM-Transformer model was used for forecasting The Fingrid open dataset was used for training and evaluating the proposed model The experimental results demonstrated the efficiency of the proposed model in solar energy production forecasting Compared to existing models and other combinations, such as LSTM-CNN, the proposed CNN-LSTM-Transformer model achieved the highest accuracy The achieved results show that the proposed model can be used as a trusted forecasting technique that facilitates the integration of solar energy into grids
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