A Machine Learning-Based Early Design Energy Prediction Framework for School Buildings Across Multiple Climatic Regions of Türkiye

ŞENEL SOLMAZ A.

Buildings, cilt.16, sa.4, 2026 (SCI-Expanded, Scopus) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 16 Sayı: 4
  • Basım Tarihi: 2026
  • Doi Numarası: 10.3390/buildings16040779
  • Dergi Adı: Buildings
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Avery, Compendex, INSPEC, Directory of Open Access Journals
  • Anahtar Kelimeler: building energy performance, surrogate modeling, machine learning in architecture, energy prediction, random forest, support vector regression, multi-layer perceptron, early design stage energy assessment
  • Dokuz Eylül Üniversitesi Adresli: Evet

Özet

School buildings are important in terms of energy performance, and their energy demand varies significantly across different climates. Early design decisions strongly influence this demand; however, building energy simulations are computationally intensive and limit rapid evaluation of alternative design options at scale. This study proposes a machine learning-based surrogate modeling framework to support early design energy assessment of school buildings across Türkiye’s six TS 825 climatic regions. A comprehensive design space is defined by varying key parameters, including building shape, orientation, window-to-wall ratio, shading, glazing systems, and insulation alternatives. Representative design configurations are generated using stratified random sampling, and then simulated in EnergyPlus, resulting in a dataset of 30,000 samples. Random Forest, Support Vector Regression, and Multilayer Perceptron models are developed within a multi-output regression framework to predict annual heating and cooling energy demand across climatic regions. The models achieve high predictive accuracy and consistent generalization, with test R2 values exceeding 0.93, while exhibiting performance differences among the evaluated algorithms. Feature importance analysis identifies window-to-wall ratio and glazing-related parameters as the most influential early design variables. Overall, the results demonstrate that machine learning-based surrogate models can substantially reduce computational effort while providing reliable, climate-responsive support for early design decision-making.