Optimizing Renewable Energy in Arid Zones: AI-Driven Forecasting and Climate Adaptation in Egypt | ||||
| مجلة الشروق للعلوم التجارية | ||||
| Volume 17, Issue 17.3, June 2025, Page 111-153 PDF (650.51 K) | ||||
| Document Type: المقالة الأصلية | ||||
| DOI: 10.21608/sjcs.2025.372773.1173 | ||||
 View on SCiNiTO | ||||
| Author | ||||
أحمد محمد صدقي خليفة  
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| El-Zohour District | ||||
| Abstract | ||||
| Egypt’s renewable energy sector must improve solar and wind power efficiency under harsh desert conditions while addressing significant data scarcity, as available information (e.g., from Benban Solar Park) is aggregated from official reports rather than raw datasets. This study employs a mixed-methods approach integrating theoretical analysis based on complexity and optimization theories (supported by global case studies from India, Chile, Morocco, the UAE, and Saudi Arabia), an extensive literature review, and comprehensive SWOT and sensitivity analyses. Forecasting is conducted using the Prophet model, with detailed calibration procedures—including cross-validation, RMSE, MAPE calculations, and error distribution analysis—compared to alternative models (e.g., LSTM, XGBoost). Prophet is prioritized for its moderate data requirements and robustness under sparse-data conditions. Graphical illustrations, such as a chart on dust impact and a color-coded SWOT diagram, are embedded (or provided as supplementary files) and referenced in the appendices to enhance clarity. Results demonstrate efficiency gains of 15–25% and energy waste reductions of 18–22%. Sensitivity analysis reveals that a ±10% change in solar irradiance leads to an approximately ±7.5% variation in system efficiency, potentially reducing carbon emissions by 10–15% (around 12,000 tons CO₂ annually). Policy recommendations emphasize modernizing grid infrastructure, enhancing data transparency through regional sharing platforms, reforming regulatory frameworks, and establishing an AI innovation fund via strategic public–private partnerships and international collaborations. | ||||
| Keywords | ||||
| Artificial Intelligence in Energy; Renewable Energy Efficiency; Climate Adaptation Strategies; Data Scarcity | ||||
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