A Novel GIS-MCDA and Artificial Intelligence Approach for Optimal Site Selection of CSP Plants with Emphasis on Comprehensive Economic Analyses (Case Study: Bushehr Province)
Subject Areas : Environment and sustainable developmentMeisam Jafari 1 , Delaram Sikarudi 2 , Sahar Ghiyas 3
1 - Department of Environment, Najafabad Branch, Islamic Azad University, Najafabad, Iran.
2 - Department of HSE Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran
3 - Najafabad Branch, Islamic Azad University, Najafabad, Iran
Keywords: Optimal site selection, Artificial Intelligence, Fuzzy, Concentrated Solar Power, GIS, Economic analysis, Bushehr.,
Abstract :
This study presents an innovative and comprehensive approach to optimal site selection for Concentrated Solar Power (CSP) plants in Bushehr Province, Iran, by integrating Geographic Information Systems (GIS), Fuzzy Multi-Criteria Decision Analysis (Fuzzy MCDA), and Artificial Intelligence techniques. The methodology encompassed processing Landsat 8 satellite imagery using the FLAASH algorithm for atmospheric corrections, calculating NDVI and LST indices, and land use classification with an overall accuracy of 87% (Kappa coefficient 0.83). Criteria weighting was performed using Analytic Hierarchy Process with a consistency ratio of 0.093. Machine learning algorithms, including Random Forest and Convolutional Neural Networks (CNN), were employed to enhance prediction accuracy, resulting in a 12.7% improvement in model precision (RMSE: 0.089 compared to 0.102 in traditional MCDA methods). According to zonal analyses on the output of the integrated artificial intelligence model and fuzzy multi-criteria assessment, the ideal areas (approximately 5.37%) were identified and extracted as highly suitable or optimal zones. Cost-Benefit Analysis (CBA) utilizing Monte Carlo simulation was conducted for economic evaluation of CSP projects. Sobol sensitivity analysis revealed that the project’s Net Present Value (NPV) is most sensitive to initial investment costs and electricity selling price. Risk assessment was performed using Value at Risk (VaR) and Conditional Value at Risk (CVaR) at a 95% confidence level. Results indicated that 46.3% of the province’s area is suitable for CSP plant construction, with three regions showing the highest potential, demonstrating positive NPV and Internal Rate of Return (IRR) above the discount rate. This research, by providing a comprehensive and innovative framework, takes a significant step in optimizing the site selection process for CSP plants and can serve as a model for similar studies in other regions.
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