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<p>Global warming, caused by greenhouse gas emissions created by human activities and urban heat island (UHI) effect, is a major issue, especially in arid regions. The study explores the energy efficiency of green-walled and green-roofed buildings in arid climates, through simulations. The study seeks to examine the effects of these greening technologies on thermal performance, heat transfer and energy consumption. The force consuming structures using green roofing and walls forecasted using deep neural network (DNN) model, which is optimised using the Deer Hunting Optimization Algorithm (DHOA). The simulation outcomes show the capacity of these greening technologies to enhance thermal comfort and mitigate UHI problems. Furthermore, these solutions contribute significantly to increased energy savings and internal air pollution. Proposed DNN–DHOA model achieved R<sup>2</sup> 0.971, RMSE 0.0181, and MAE 0.0119 demonstrating superior predictive performance in energy consumption estimation task. The findings provide valuable insights to urban designers and policymakers in arid regions, promoting the integration of green technologies to enhance the sustainability and energy efficiency of urban environments.</p>

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Simulation of energy performance in buildings featuring green walls and green roofs in arid climates

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月2日

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  • Hasibullah Khan,
  • Fazalrahman Ikhlas,
  • Ahmad Jawad Niazi,
  • Ahmad Zubair Seddiqi

摘要

Global warming, caused by greenhouse gas emissions created by human activities and urban heat island (UHI) effect, is a major issue, especially in arid regions. The study explores the energy efficiency of green-walled and green-roofed buildings in arid climates, through simulations. The study seeks to examine the effects of these greening technologies on thermal performance, heat transfer and energy consumption. The force consuming structures using green roofing and walls forecasted using deep neural network (DNN) model, which is optimised using the Deer Hunting Optimization Algorithm (DHOA). The simulation outcomes show the capacity of these greening technologies to enhance thermal comfort and mitigate UHI problems. Furthermore, these solutions contribute significantly to increased energy savings and internal air pollution. Proposed DNN–DHOA model achieved R2 0.971, RMSE 0.0181, and MAE 0.0119 demonstrating superior predictive performance in energy consumption estimation task. The findings provide valuable insights to urban designers and policymakers in arid regions, promoting the integration of green technologies to enhance the sustainability and energy efficiency of urban environments.