{"title":"利用模糊推理系统选择太阳能发电厂厂址：伊朗案例研究","authors":"M. R. Mehrian, M. M. Qelichi, H. Tahouri","doi":"10.1007/s13762-024-06047-z","DOIUrl":null,"url":null,"abstract":"<p>Solar energy, recognized for its potential in direct conversion into electricity and heat, offers a sustainable energy source with minimal environmental impact. Despite Iran’s significant solar potential, the country’s reliance on fossil fuels has hindered the widespread adoption of solar energy. This study evaluates the relative potential of different regions in Iran for solar power plant development using a novel integration of Geographic Information Systems (GIS) and Fuzzy Inference Systems (FIS). The findings indicate that the most suitable regions for photovoltaic (PV) power plant construction are located in Isfahan, Khorasan-Razavi, and Kerman provinces, where over 70% of the highly suitable land is identified. Conversely, the northern provinces such as Gilan, Mazandaran, and Golestan show minimal suitability. The study’s outcomes provide critical insights for policymakers and stakeholders to optimize site selection for solar energy infrastructure, potentially increasing Iran’s renewable energy capacity significantly.</p>","PeriodicalId":589,"journal":{"name":"International Journal of Environmental Science and Technology","volume":"25 1","pages":""},"PeriodicalIF":3.0000,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Solar power plant site selection using fuzzy inference system: a case study in Iran\",\"authors\":\"M. R. Mehrian, M. M. Qelichi, H. Tahouri\",\"doi\":\"10.1007/s13762-024-06047-z\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Solar energy, recognized for its potential in direct conversion into electricity and heat, offers a sustainable energy source with minimal environmental impact. Despite Iran’s significant solar potential, the country’s reliance on fossil fuels has hindered the widespread adoption of solar energy. This study evaluates the relative potential of different regions in Iran for solar power plant development using a novel integration of Geographic Information Systems (GIS) and Fuzzy Inference Systems (FIS). The findings indicate that the most suitable regions for photovoltaic (PV) power plant construction are located in Isfahan, Khorasan-Razavi, and Kerman provinces, where over 70% of the highly suitable land is identified. Conversely, the northern provinces such as Gilan, Mazandaran, and Golestan show minimal suitability. The study’s outcomes provide critical insights for policymakers and stakeholders to optimize site selection for solar energy infrastructure, potentially increasing Iran’s renewable energy capacity significantly.</p>\",\"PeriodicalId\":589,\"journal\":{\"name\":\"International Journal of Environmental Science and Technology\",\"volume\":\"25 1\",\"pages\":\"\"},\"PeriodicalIF\":3.0000,\"publicationDate\":\"2024-09-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Environmental Science and Technology\",\"FirstCategoryId\":\"93\",\"ListUrlMain\":\"https://doi.org/10.1007/s13762-024-06047-z\",\"RegionNum\":4,\"RegionCategory\":\"环境科学与生态学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENVIRONMENTAL SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Environmental Science and Technology","FirstCategoryId":"93","ListUrlMain":"https://doi.org/10.1007/s13762-024-06047-z","RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
Solar power plant site selection using fuzzy inference system: a case study in Iran
Solar energy, recognized for its potential in direct conversion into electricity and heat, offers a sustainable energy source with minimal environmental impact. Despite Iran’s significant solar potential, the country’s reliance on fossil fuels has hindered the widespread adoption of solar energy. This study evaluates the relative potential of different regions in Iran for solar power plant development using a novel integration of Geographic Information Systems (GIS) and Fuzzy Inference Systems (FIS). The findings indicate that the most suitable regions for photovoltaic (PV) power plant construction are located in Isfahan, Khorasan-Razavi, and Kerman provinces, where over 70% of the highly suitable land is identified. Conversely, the northern provinces such as Gilan, Mazandaran, and Golestan show minimal suitability. The study’s outcomes provide critical insights for policymakers and stakeholders to optimize site selection for solar energy infrastructure, potentially increasing Iran’s renewable energy capacity significantly.
期刊介绍：
International Journal of Environmental Science and Technology (IJEST) is an international scholarly refereed research journal which aims to promote the theory and practice of environmental science and technology, innovation, engineering and management.
A broad outline of the journal''s scope includes: peer reviewed original research articles, case and technical reports, reviews and analyses papers, short communications and notes to the editor, in interdisciplinary information on the practice and status of research in environmental science and technology, both natural and man made.
The main aspects of research areas include, but are not exclusive to; environmental chemistry and biology, environments pollution control and abatement technology, transport and fate of pollutants in the environment, concentrations and dispersion of wastes in air, water, and soil, point and non-point sources pollution, heavy metals and organic compounds in the environment, atmospheric pollutants and trace gases, solid and hazardous waste management; soil biodegradation and bioremediation of contaminated sites; environmental impact assessment, industrial ecology, ecological and human risk assessment; improved energy management and auditing efficiency and environmental standards and criteria.
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