Scholarlink

<p>This research analyzed and evaluated the factors affecting the productivity of solar, wave, and wind renewable energy systems in the coastal city of Tonekabon. We employed statistical and machine learning methods, including Linear Regression and Random Forest, on a dataset of spatial, climatic, and physical data from the region. This analysis identified key indicators and predicted the optimal performance of energy systems. A correlation analysis was also conducted to examine the relationships between these indicators. For the first time in this region, the role of graphene-based photovoltaic (GPV) was investigated. The results indicate that indicators such as optimal building orientation, height, density, vegetation cover, and climatic parameters significantly impact renewable energy productivity. Furthermore, GPV demonstrated a 13.8% improvement in annual average efficiency compared to conventional silicon cells in Tonekabon’s humid climate. This research proposes a framework for improving urban design and promoting sustainable energy development in coastal cities like Tonekabon.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

An AI and multi-criteria framework for siting renewable energy systems enhanced by graphene photovoltaics in a coastal City

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月27日

查看全文
  • Farshad Rafipour,
  • Manouchehr Tabibian,
  • Mehdi Saati

摘要

This research analyzed and evaluated the factors affecting the productivity of solar, wave, and wind renewable energy systems in the coastal city of Tonekabon. We employed statistical and machine learning methods, including Linear Regression and Random Forest, on a dataset of spatial, climatic, and physical data from the region. This analysis identified key indicators and predicted the optimal performance of energy systems. A correlation analysis was also conducted to examine the relationships between these indicators. For the first time in this region, the role of graphene-based photovoltaic (GPV) was investigated. The results indicate that indicators such as optimal building orientation, height, density, vegetation cover, and climatic parameters significantly impact renewable energy productivity. Furthermore, GPV demonstrated a 13.8% improvement in annual average efficiency compared to conventional silicon cells in Tonekabon’s humid climate. This research proposes a framework for improving urban design and promoting sustainable energy development in coastal cities like Tonekabon.