10.57647/ijeee.2026.1701.06

Optimization of Energy Consumption in Office Buildings: A Sustainable Design and Novel Materials Approach

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  • Hedieh Dabiri1,
  • Rasul Pahlavanpour1,
  1. Department of Architecture, ST.C, Islamic Azad University, Tehran, Iran

Received: 2025-12-02

Revised: 2026-04-05

Accepted: 2026-05-01

Published in Issue 2026-03-31

Copyright (c) 2026 Hedieh Dabiri, Rasul Pahlavanpour (Author)

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Dabiri, H., & Pahlavanpour, R. (2026). Optimization of Energy Consumption in Office Buildings: A Sustainable Design and Novel Materials Approach. International Journal of Energy and Environmental Engineering, 17(01). https://doi.org/10.57647/ijeee.2026.1701.06
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Abstract

Optimizing energy performance and achieving sustainable design in buildings are critical global challenges. This study addresses these challenges by examining the Molla Sadra Administrative-Educational Building as a case study for data‑driven sustainable design. Using a mixed‑methods approach, the research integrates theoretical analysis, energy simulation, life cycle assessment (LCA), and machine learning (ML) applied to twelve months of operational data. Advanced models, including Random Forest and MLP, achieved an accuracy of 92% in predicting energy consumption and identified three primary influencing factors: occupancy (38%), solar radiation (24%), and ventilation (19%). Based on these insights, dynamic temperature adjustments between 22 °C and 25 °C were recommended, leading to an estimated 18% reduction in energy use. The discussion highlights the synergy between sustainable architectural design and intelligent data utilization in enhancing building performance. Overall, the Molla Sadra project is positioned as a benchmark for sustainable, data‑driven buildings that achieve substantial reductions in both energy consumption and carbon emissions.

Keywords

  • Sustainable architecture,
  • Energy performance,
  • Machine learning,
  • Life cycle assessment,
  • Data driven design
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