Solar Energy Production Forecasting Based on a Hybrid CNN-LSTM-Transformer Model

Elham M. Al-Ali (), Yassine Hajji, Yahia Said, Manel Hleili, Amal M. Alanzi, Ali H. Laatar and Mohamed Atri
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Elham M. Al-Ali: Mathematics Department, College of Sciences, Tabuk University, Tabuk 71491, Saudi Arabia
Yassine Hajji: Laboratory of Energetics and Thermal and Mass Transfer (LR01ES07), Faculty of Sciences of Tunis, University of Tunis El Manar, Tunis 1068, Tunisia
Yahia Said: Remote Sensing Unit, College of Engineering, Northern Border University, Arar 91431, Saudi Arabia
Manel Hleili: Mathematics Department, College of Sciences, Tabuk University, Tabuk 71491, Saudi Arabia
Amal M. Alanzi: Mathematics Department, College of Sciences, Tabuk University, Tabuk 71491, Saudi Arabia
Ali H. Laatar: Physics Department, College of Sciences, Tabuk University, Tabuk 71491, Saudi Arabia
Mohamed Atri: College of Computer Sciences, King Khalid University, Abha 62529, Saudi Arabia

Mathematics, 2023, vol. 11, issue 3, 1-19

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.

Keywords: solar energy production; forecasting; convolutional neural network; long short-term memory network; transformer (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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