A novel hybrid model to evaluate the location of net-zero energy consumption building based on remote sensing, analysis hierarchical process and machine learning
Kong J. Mamyrbayev O. Abed A.M.
15 August 2025Elsevier Ltd
Energy
2025#329
The design and architecture of buildings are critical stages in construction, with significant emphasis on environmental sustainability and energy efficiency. Achieving a net-zero energy architectural design necessitates careful consideration of the buildings location. This simulation-based study introduces a novel hybrid model that integrates remote sensing (RS) techniques with the analytical hierarchy process (AHP) and a machine learning (ML) algorithm (M5 model tree) to optimize and validate site selection for net-zero energy buildings (NZEBs) in Xian, China. The proposed approach enhances decision-making accuracy by leveraging RS data and ML-based validation for robustness. Simulated datasets, derived from RS and applied scenarios, were used to evaluate key factors such as solar energy radiation, climatic conditions, green space distribution, accessibility, population density, and proximity to renewable energy sources. The results demonstrated that solar energy radiation, accessibility, and proximity to renewable energy sources were the most influential factors, 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, respectively. Thirteen urban areas of Xian 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, validating the consistency and robustness of the decision-making framework. The findings provide practical aspect for urban planners and policymakers, offering a data-driven framework that ensures NZEB developments align with energy efficiency goals and environmental policies.
Analytical hierarchy process (AHP) , M5 model tree , Net-zero energy buildings (NZEBs) , Remote sensing (RS) , Site selection
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School of Economics and Management, Tianjin Chengjian University, Tianjin, 300384, China
School of Architecture, Tianjin University, Tianjin, 300072, China
Institute of Information and Computational Technologies, Almaty, Kazakhstan
Air Conditioning and Refrigeration Techniques Engineering Department, College of Engineering and Technologies, Al-Mustaqbal University, Babylon, Hillah, 51001, Iraq
Al-Mustaqbal Center for Energy Research, Al-Mustaqbal University, Babylon, 51001, Iraq
School of Economics and Management
School of Architecture
Institute of Information and Computational Technologies
Air Conditioning and Refrigeration Techniques Engineering Department
Al-Mustaqbal Center for Energy Research
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