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

Mohammed A. Bou-Rabee, Muhammad Yasin Naz, Imad ED. Albalaa and Shaharin Anwar Sulaiman
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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

Energies, 2022, vol. 15, issue 6, 1-12

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.

Keywords: solar radiation prediction; wavelet decomposition; coevolutionary neural network; attention-based bidirectional long short-term memory (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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