رویکرد نوینGIS-MCDA و هوش مصنوعی در مکان‌یابی بهینه نیروگاه‌های CSP با تأکید بر تحلیل‌های جامع اقتصادی (مطالعه موردی: استان بوشهر)
محورهای موضوعی : محیط زیست و توسعه پایدارمیثم جعفری 1 , دلارام سیکارودی 2 , سحر غیاث 3
1 - گروه مهندسی محیط زیست و HSE، دانشگاه آزاد اسلامی واحد نجف آباد، ایران
2 - گروه مهندسی ایمنی، بهداشت و محیط زیست، واحد نجف آباد، دانشگاه آزاد اسلامی، نجف آباد، ایران.
3 - واحد نجف آباد، دانشگاه آزاد اسلامی، نجف آباد، ایران.
کلید واژه: مکان‌یابی بهینه, هوش مصنوعی, فازی, نیروگاه حرارتی خورشیدی, GIS, تحلیل اقتصادی, بوشهر.,
چکیده مقاله :
این پژوهش با هدف ارائه رویکردی نوین و جامع در مکان‌یابی بهینه نیروگاه‌های حرارتی خورشیدی (CSP) در استان بوشهر، ترکیبی از سیستم اطلاعات جغرافیایی (GIS)، تحلیل چند معیاره فازی (Fuzzy MCDA) و تکنیک‌های هوش مصنوعی را به کار گرفته است. روش‌شناسی پژوهش شامل پردازش تصاویر ماهواره‌ای Landsat 8 با استفاده از الگوریتم FLAASH برای تصحیحات اتمسفری، محاسبه شاخص‌های NDVI و LST، و طبقه‌بندی کاربری اراضی با دقت کلی 87% (ضریب کاپا 0.83) بود. وزن‌دهی معیارها با استفاده از فرآیند تحلیل سلسله مراتبی انجام شد، با ضریب سازگاری 0.093. الگوریتم‌های یادگیری ماشین شامل Random Forest و CNN برای بهبود دقت پیش‌بینی‌ها به کار گرفته شدند، که منجر به افزایش 12.7% در دقت مدل شد (RMSE: 0.089 در مقابل 0.102 در روش‌های سنتی MCDA). طبق تحلیل‌های Zonal روی خروجی مدل تلفیقی هوش مصنوعی و ارزیابی چند معیاره فازی، پهنه های ایده آل (تقریباً ٪۵.۳۷)، به عنوان مناطق بسیار مناسب یا بهینه شناسایی و استخراج گردیدند. تحلیل هزینه-فایده (CBA) با استفاده از شبیه‌سازی مونت کارلو برای ارزیابی اقتصادی پروژه‌های CSP انجام شد. تحلیل حساسیت Sobol نشان داد که NPV پروژه بیشترین حساسیت را نسبت به هزینه سرمایه‌گذاری اولیه و قیمت فروش برق دارد. ارزیابی ریسک با استفاده از VaR و CVaR در سطح اطمینان 95% انجام شد. این پژوهش با ارائه چارچوبی جامع و نوآورانه، گامی مهم در بهینه‌سازی فرآیند مکان‌یابی نیروگاه‌های CSP برداشته و می‌تواند به عنوان الگویی برای مطالعات مشابه در سایر مناطق مورد استفاده قرار گیرد.
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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