Novel Hybrid Model for Net-Zero Energy Building Design

Strategic Insights -

Research from Tianjin Chengjian University introduces a simulation-based study that integrates remote sensing, analytical hierarchy process, and machine learning to optimize site selection for net-zero energy buildings in Xi'an, China. The study finds that solar energy radiation, accessibility, and proximity to renewable energy sources are the most influential factors in site selection. The proposed approach enhances decision-making accuracy and provides a data-driven framework for urban planners and policymakers to ensure NZEB developments align with energy efficiency goals and environmental policies.

Key Takeaways:

  • The study integrates remote sensing, analytical hierarchy process, and machine learning to optimize site selection for net-zero energy buildings in Xi'an, China.
  • The proposed hybrid model enhances decision-making accuracy by leveraging remote sensing data and machine learning-based validation for robustness.
  • The results indicate that solar energy radiation, accessibility, and proximity to renewable energy sources are the most influential factors in site selection, with weights of 0.40, 0.25, and 0.20 in the AHP analysis and 0.42, 0.25, and 0.18 in the M5 model tree validation.
  • Thirteen urban areas of Xi'an were analyzed, and the results indicated that Zones 3, 6, and 8 received the highest suitability scores, while Zones 9, 12, and 13 were deemed the least suitable for NZEB projects.
  • The hybrid model demonstrated high accuracy, with a Pearson correlation coefficient of 0.91 confirming strong agreement between the AHP-derived weights and the M5 model tree predictions.
  • The research provides practical aspects for urban planners and policymakers, offering a data-driven framework that ensures NZEB developments align with energy efficiency goals and environmental policies.

Statistics:

  • 13 urban areas of Xi'an were analyzed.
  • The weights of solar energy radiation, accessibility, and proximity to renewable energy sources in site selection were 0.40, 0.25, and 0.20 in the AHP analysis.
  • The weights of solar energy radiation, accessibility, and proximity to renewable energy sources in site selection were 0.42, 0.25, and 0.18 in the M5 model tree validation.
  • The Pearson correlation coefficient was 0.91, confirming strong agreement between the AHP-derived weights and the M5 model tree predictions.
  • The research received a Pearson correlation coefficient of 0.91.

Sources:

  • A Novel Hybrid Model To Evaluate the Location of Net-zero Energy Consumption Building Based On Remote Sensing, Analysis Hierarchical Process and Machine Learning. Energy, 2025;329. Energy can be contacted at: Pergamon-elsevier Science Ltd, The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, England. (Elsevier - www.elsevier.com; Energy - www.journals.elsevier.com/energy/)
  • Reports from Tianjin Chengjian University Describe Recent Advances in Renewable Energy (A Novel Hybrid Model To Evaluate the Location of Net-zero Energy Consumption Building Based On Remote Sensing, Analysis Hierarchical Process and Machine ...). Ecology, Environment & Conservation. August 22, 2025; p 391.