Spatio-Temporal Variation in Solar Irradiance in the Mediterranean Region: A Deep Learning Approach

Buket İşler (), Uğur Şener, Ahmet Tokgözlü, Zafer Aslan and Rene Heise
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Buket İşler: Department of Software Engineering, Istanbul Topkapi University, 34087 Istanbul, Turkey
Uğur Şener: Department of Computer Information Systems and Business Analytics, Metropolitan State University of Denver, Denver, CO 80217, USA
Ahmet Tokgözlü: Faculty of Science and Literature, Süleyman Demirel University, 32260 Isparta, Turkey
Zafer Aslan: Department of Computer Engineering, Faculty of Engineering, Istanbul Aydın University, 34295 Istanbul, Turkey
Rene Heise: VNC, International Staff—IHC, 1110 Brussels, Belgium

Sustainability, 2025, vol. 17, issue 15, 1-25

Abstract: In response to the global imperative of reducing greenhouse gas emissions, the optimisation of renewable energy systems under regionally favourable conditions has become increasingly essential. Solar irradiance forecasting plays a pivotal role in enhancing energy planning, grid reliability, and long-term sustainability. However, in the context of Turkey, existing studies on solar radiation forecasting often rely on traditional statistical approaches and are limited to single-site analyses, with insufficient attention to regional diversity and deep learning-based modelling. To address this gap, the present study focuses on Turkey’s Mediterranean region, characterised by high solar potential and diverse climatic conditions and strategically relevant to national clean energy targets. Historical data from 2020 to 2023 were used to forecast solar irradiance patterns up to 2026. Five representative locations—Adana, Isparta, Fethiye, Ulukışla, and Yüreğir—were selected to capture spatial and temporal variability across inland, coastal, and high-altitude zones. Advanced deep learning models, including artificial neural networks (ANN), long short-term memory (LSTM), and bidirectional LSTM (BiLSTM), were developed and evaluated using standard performance metrics. Among these, BiLSTM achieved the highest accuracy, with a correlation coefficient of R = 0.95, RMSE = 0.22, and MAPE = 5.4% in Fethiye, followed by strong performance in Yüreğir (R = 0.90, RMSE = 0.12, MAPE = 7.2%). These results demonstrate BiLSTM’s superior capacity to model temporal dependencies and regional variability in solar radiation. The findings contribute to the development of location-specific forecasting frameworks and offer valuable insights for renewable energy planning and grid integration in solar-rich environments.

Keywords: solar irradiance forecasting; Mediterranean region; deep learning models; renewable energy systems (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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