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AI-enhanced techno-economic and environmental optimization for nearly zero-energy building retrofitting: a case study of university campus
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  • Published: 23 March 2026

AI-enhanced techno-economic and environmental optimization for nearly zero-energy building retrofitting: a case study of university campus

  • Mohammad Alobaid1,
  • Ahmed G. Abo-Khalil1,2 &
  • Khairy Sayed3,4,5 

Scientific Reports , Article number:  (2026) Cite this article

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Subjects

  • Energy and society
  • Energy science and technology
  • Engineering
  • Environmental sciences
  • Environmental social sciences

Abstract

This study presents an AI-enhanced framework for the techno-economic and environmental optimization of nearly zero-energy building (nZEB) retrofitting strategies, demonstrated through a real-world case study at a university campus. The proposed methodology integrates dynamic energy modeling, photovoltaic (PV) system simulation, and artificial intelligence-based optimization to identify retrofit solutions that balance energy efficiency, financial viability, and carbon emissions reduction. Key performance indicators, including the levelized cost of electricity (LCOE), return on investment (ROI), internal rate of return (IRR), energy use intensity (EUI), and cumulative CO2 savings, are analyzed over a 25-year horizon. The study further accounts for improving grid emission factors and dynamic electricity tariffs, enhancing the accuracy of long-term sustainability projections. Results reveal that AI-driven decision support can significantly optimize retrofit pathways, achieving substantial CO2 reduction, financial returns, and renewable energy contributions that surpass the 50% nZEB threshold. The proposed framework offers a scalable, data-driven tool for policymakers, facility managers, and energy planners aiming to accelerate the transition toward decarbonized, resilient built environments.

Data availability

All data generated or analyzed during this study are included in this published article.

Abbreviations

GCC:

Gulf Cooperation Council

nZEB:

Nearly zero-energy building

EUI:

Energy use intensity

PV:

Photovoltaic

CSP:

Concentrated solar power

HVAC:

Heating, ventilation, and air conditioning

BMS:

Building management system

LCOE:

Levelized cost of electricity

ROI:

Return on investment

IRR:

Internal rate of return

NPV:

Net present value

GHI:

Global horizontal irradiance

DNI:

Direct normal irradiance

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Acknowledgements

The author extends the appreciation to the Deanship of Postgraduate Studies and Scientific Research at Majmaah University for funding this research work through the project number (R-2026-76).

Author information

Authors and Affiliations

  1. Department of Mechanical and Industrial Engineering, College of Engineering, Majmaah University, 11952, Almajmaah, Saudi Arabia

    Mohammad Alobaid & Ahmed G. Abo-Khalil

  2. Dept. of Sustainable and Renewable Energy Engineering, University of Sharjah, Sharjah, United Arab Emirates

    Ahmed G. Abo-Khalil

  3. Department of Electrical Engineering, College of Engineering, Assuit University, Assuit, Egypt

    Khairy Sayed

  4. City College of New York, New York, NY, 10031, USA

    Khairy Sayed

  5. Department of Electrical Engineering, College of Engineering, Sohag University, Sohag, Egypt

    Khairy Sayed

Authors
  1. Mohammad Alobaid
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  2. Ahmed G. Abo-Khalil
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  3. Khairy Sayed
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Contributions

A.A. (Ahmed Abo-Khalil) conceived the research concept, designed the methodology, supervised the experimental work, distributed the research tasks among the co-authors, and wrote the first draft of the manuscript.K.S (Khairy Sayed) conducted simulations, analyzed the experimental data, and contributed to result interpretation.M. A. (Mohammed Alobaid) prepared figures, organized datasets, and participated in manuscript revision. contributed to the literature review, validation of results, and editing of the final draft.All authors discussed the results, reviewed, and approved the final version of the manuscript.

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Correspondence to Ahmed G. Abo-Khalil.

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Alobaid, M., Abo-Khalil, A.G. & Sayed, K. AI-enhanced techno-economic and environmental optimization for nearly zero-energy building retrofitting: a case study of university campus. Sci Rep (2026). https://doi.org/10.1038/s41598-026-41747-1

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  • Received: 22 December 2025

  • Accepted: 23 February 2026

  • Published: 23 March 2026

  • DOI: https://doi.org/10.1038/s41598-026-41747-1

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Keywords

  • AI-enhanced optimization
  • Nearly zero-energy building
  • Building retrofitting
  • Techno-economic analysis
  • Environmental optimization
  • dynamic energy modeling
  • Photovoltaic (PV) simulation
  • Artificial intelligence
  • Levelized cost of electricity (LCOE)
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