A novel integrated GIS-AI framework for optimal CSP plant site selection: a multi-criteria approach under climate change scenarios in Bushehr, Iran

IF 2.8 3区地球科学 Q2 ASTRONOMY & ASTROPHYSICS

Advances in Space Research Pub Date : 2025-08-15 DOI:10.1016/j.asr.2025.08.028

Sahar Ghiyas , Delaram Sikarudi , Meisam Jafari

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引用次数: 0

Abstract

Optimal site selection for Concentrated Solar Power (CSP) plants remains a significant challenge in renewable energy planning, particularly in regions facing climate change impacts. This study addresses this challenge by developing an integrated framework for CSP site selection in Bushehr Province, Iran. We employed Geographic Information Systems with fuzzy multi-criteria decision analysis to evaluate 18 criteria across six categories (climatic, topographic, infrastructural, environmental, socio-economic, and technical). The Analytic Hierarchy Process determined criteria weights, while climate projections were generated using the RegCM4 model under the RCP 4.5 scenario. Machine learning algorithms, specifically Random Forest and Convolutional Neural Networks, enhanced spatial prediction accuracy by 12.7 % compared to conventional methods. Results identified 5.37 % (approximately 1246 km2) of the province as highly suitable for CSP development, with these areas demonstrating positive economic returns under Monte Carlo simulation-based Cost-Benefit Analysis. This framework’s primary innovation lies in its seamless integration of spatial analysis, climate modeling, and artificial intelligence techniques to support sustainable energy planning in arid regions experiencing climate variability.

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一种新的集成GIS-AI框架，用于优化光热电站选址：伊朗布什尔气候变化情景下的多标准方法

聚光太阳能（CSP）电站的最佳选址仍然是可再生能源规划中的重大挑战，特别是在面临气候变化影响的地区。本研究通过为伊朗布什尔省的CSP选址制定一个综合框架来解决这一挑战。我们使用地理信息系统和模糊多标准决策分析来评估6个类别（气候、地形、基础设施、环境、社会经济和技术）中的18个标准。层次分析法确定了标准权重，而在RCP 4.5情景下使用RegCM4模式生成气候预估。机器学习算法，特别是随机森林和卷积神经网络，与传统方法相比，空间预测精度提高了12.7%。结果表明，该省5.37%（约1246平方公里）的土地非常适合开发光热发电，根据基于蒙特卡洛模拟的成本效益分析，这些地区显示出积极的经济回报。该框架的主要创新在于将空间分析、气候建模和人工智能技术无缝结合，以支持经历气候变化的干旱地区的可持续能源规划。

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来源期刊

Advances in Space Research 地学天文-地球科学综合

CiteScore

5.20

自引率

11.50%

发文量

800

审稿时长

5.8 months

期刊介绍： The COSPAR publication Advances in Space Research (ASR) is an open journal covering all areas of space research including: space studies of the Earth''s surface, meteorology, climate, the Earth-Moon system, planets and small bodies of the solar system, upper atmospheres, ionospheres and magnetospheres of the Earth and planets including reference atmospheres, space plasmas in the solar system, astrophysics from space, materials sciences in space, fundamental physics in space, space debris, space weather, Earth observations of space phenomena, etc. NB: Please note that manuscripts related to life sciences as related to space are no more accepted for submission to Advances in Space Research. Such manuscripts should now be submitted to the new COSPAR Journal Life Sciences in Space Research (LSSR). All submissions are reviewed by two scientists in the field. COSPAR is an interdisciplinary scientific organization concerned with the progress of space research on an international scale. Operating under the rules of ICSU, COSPAR ignores political considerations and considers all questions solely from the scientific viewpoint.