IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v330y2025ics0360544225024764.html
   My bibliography  Save this article

Data-driven framework for energy optimizing net-zero energy buildings (NZEB): A functional assessment of energy efficiency and thermal comfort

Author

Listed:
  • Alghassab, Mohammed A.

Abstract

This study presents a comprehensive AI-driven framework for evaluating energy consumption and optimizing the performance of Net-Zero Energy Buildings (NZEB) in contrasting climates. Focusing on Riyadh, Saudi Arabia, and Lhasa, China, Long Short-Term Memory (LSTM) networks were utilized to predict energy consumption patterns in educational and office buildings. The model achieved a mean absolute percentage error (MAPE) of 0.81 %–4.85 % in energy consumption predictions and an error range of 0.65 %–2.41 % for thermal comfort predictions. Sensitivity analysis revealed that a 10 % reduction in cooling demand could result in up to a 7.2 % decrease in operational costs. Furthermore, the economic analysis indicated that implementing data-driven energy efficiency measures could lead to a 12.5 % reduction in greenhouse gas emissions. Policy implications were also addressed, showing that integrating these models into urban planning could enhance resource management by 15 %, fostering sustainable urban growth. Sensitivity analysis revealed that climatic parameters and mechanical ventilation had the most significant impact on energy consumption. Furthermore, the economic model indicated that initial investments in energy optimization technologies resulted in a substantial return on investment (ROI) over 10 years, with an average ROI of 185 %. Probabilistic distribution analysis showed distinct energy consumption patterns between educational and office buildings, underscoring the need for tailored energy management strategies.

Suggested Citation

  • Alghassab, Mohammed A., 2025. "Data-driven framework for energy optimizing net-zero energy buildings (NZEB): A functional assessment of energy efficiency and thermal comfort," Energy, Elsevier, vol. 330(C).
    • Handle: RePEc:eee:energy:v:330:y:2025:i:c:s0360544225024764
    DOI: 10.1016/j.energy.2025.136834
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544225024764
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2025.136834?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Samuel Moveh & Emmanuel Alejandro Merchán-Cruz & Maher Abuhussain & Yakubu Aminu Dodo & Saleh Alhumaid & Ali Hussain Alhamami, 2025. "Deep Learning Framework Using Transformer Networks for Multi Building Energy Consumption Prediction in Smart Cities," Energies, MDPI, vol. 18(6), pages 1-22, March.
    2. Gulzhanat Akhanova & Abid Nadeem & Jong R. Kim & Salman Azhar, 2019. "A Framework of Building Sustainability Assessment System for the Commercial Buildings in Kazakhstan," Sustainability, MDPI, vol. 11(17), pages 1-24, August.
    3. Fumo, Nelson & Rafe Biswas, M.A., 2015. "Regression analysis for prediction of residential energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 47(C), pages 332-343.
    4. Wei, Wu & Skye, Harrison M., 2021. "Residential net-zero energy buildings: Review and perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 142(C).
    5. Zhuang, Dian & Gan, Vincent J.L. & Duygu Tekler, Zeynep & Chong, Adrian & Tian, Shuai & Shi, Xing, 2023. "Data-driven predictive control for smart HVAC system in IoT-integrated buildings with time-series forecasting and reinforcement learning," Applied Energy, Elsevier, vol. 338(C).
    6. Cheng, Ziwei & Yao, Zhen, 2024. "A novel approach to predict buildings load based on deep learning and non-intrusive load monitoring technique, toward smart building," Energy, Elsevier, vol. 312(C).
    7. Khaled Almazam & Omar Humaidan & Nahla M. Shannan & Faizah Mohammed Bashir & Taha Gammoudi & Yakubu Aminu Dodo, 2025. "Innovative Energy Efficiency in HVAC Systems with an Integrated Machine Learning and Model Predictive Control Technique: A Prospective Toward Sustainable Buildings," Sustainability, MDPI, vol. 17(7), pages 1-35, March.
    8. Faizah Mohammed Bashir & Yakubu Aminu & Mohamed Ahmed & Norita Md Norwawi & Nahla M Shannan & Amirhossein Aghajani Afghan, 2024. "Effects of natural light on improving the lighting and energy efficiency of buildings: toward low energy consumption and CO2 emission," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 19, pages 296-305.
    9. Mohamed Issa & Mohamed Attalla & Jeff Rankin & A. John Christian, 2011. "Energy consumption in conventional, energy-retrofitted and green LEED Toronto schools," Construction Management and Economics, Taylor & Francis Journals, vol. 29(4), pages 383-395.
    10. Gao, Zhikun & Yang, Siyuan & Yu, Junqi & Zhao, Anjun, 2024. "Hybrid forecasting model of building cooling load based on combined neural network," Energy, Elsevier, vol. 297(C).
    11. Fan, Cheng & Xiao, Fu & Wang, Shengwei, 2014. "Development of prediction models for next-day building energy consumption and peak power demand using data mining techniques," Applied Energy, Elsevier, vol. 127(C), pages 1-10.
    12. Abdo Abdullah Ahmed Gassar & Choongwan Koo & Tae Wan Kim & Seung Hyun Cha, 2021. "Performance Optimization Studies on Heating, Cooling and Lighting Energy Systems of Buildings during the Design Stage: A Review," Sustainability, MDPI, vol. 13(17), pages 1-47, September.
    13. Amasyali, Kadir & El-Gohary, Nora M., 2018. "A review of data-driven building energy consumption prediction studies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1192-1205.
    14. Yan, Biao & Yang, Wansheng & He, Fuquan & Zeng, Wenhao, 2023. "Occupant behavior impact in buildings and the artificial intelligence-based techniques and data-driven approach solutions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 184(C).
    15. Kang, Yiting & Zhang, Dongjie & Cui, Yu & Xu, Wei & Lu, Shilei & Wu, Jianlin & Hu, Yiqun, 2024. "Integrated passive design method optimized for carbon emissions, economics, and thermal comfort of zero-carbon buildings," Energy, Elsevier, vol. 295(C).
    16. Feng, Wei & Zhang, Qianning & Ji, Hui & Wang, Ran & Zhou, Nan & Ye, Qing & Hao, Bin & Li, Yutong & Luo, Duo & Lau, Stephen Siu Yu, 2019. "A review of net zero energy buildings in hot and humid climates: Experience learned from 34 case study buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 114(C), pages 1-1.
    17. Liang, Rui & Wang, Po-Hsun, 2024. "Enhancing energy efficiency in buildings, optimization method and building management systems application for lower CO2 emissions," Energy, Elsevier, vol. 313(C).
    18. Jahangir Hossain & Aida. F. A. Kadir & Ainain. N. Hanafi & Hussain Shareef & Tamer Khatib & Kyairul. A. Baharin & Mohamad. F. Sulaima, 2023. "A Review on Optimal Energy Management in Commercial Buildings," Energies, MDPI, vol. 16(4), pages 1-40, February.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Chou, Jui-Sheng & Tran, Duc-Son, 2018. "Forecasting energy consumption time series using machine learning techniques based on usage patterns of residential householders," Energy, Elsevier, vol. 165(PB), pages 709-726.
    2. Lara Ramadan & Isam Shahrour & Hussein Mroueh & Fadi Hage Chehade, 2021. "Use of Machine Learning Methods for Indoor Temperature Forecasting," Future Internet, MDPI, vol. 13(10), pages 1-18, September.
    3. Ding, Zhikun & Chen, Weilin & Hu, Ting & Xu, Xiaoxiao, 2021. "Evolutionary double attention-based long short-term memory model for building energy prediction: Case study of a green building," Applied Energy, Elsevier, vol. 288(C).
    4. Deb, Chirag & Dai, Zhonghao & Schlueter, Arno, 2021. "A machine learning-based framework for cost-optimal building retrofit," Applied Energy, Elsevier, vol. 294(C).
    5. Zhang, Liang & Wen, Jin & Li, Yanfei & Chen, Jianli & Ye, Yunyang & Fu, Yangyang & Livingood, William, 2021. "A review of machine learning in building load prediction," Applied Energy, Elsevier, vol. 285(C).
    6. Mohammad Mahdi Forootan & Iman Larki & Rahim Zahedi & Abolfazl Ahmadi, 2022. "Machine Learning and Deep Learning in Energy Systems: A Review," Sustainability, MDPI, vol. 14(8), pages 1-49, April.
    7. Fan, Cheng & Sun, Yongjun & Zhao, Yang & Song, Mengjie & Wang, Jiayuan, 2019. "Deep learning-based feature engineering methods for improved building energy prediction," Applied Energy, Elsevier, vol. 240(C), pages 35-45.
    8. Fan, Cheng & Sun, Yongjun & Xiao, Fu & Ma, Jie & Lee, Dasheng & Wang, Jiayuan & Tseng, Yen Chieh, 2020. "Statistical investigations of transfer learning-based methodology for short-term building energy predictions," Applied Energy, Elsevier, vol. 262(C).
    9. Yuwen You & Zhonghua Wang & Zhihao Liu & Chunmei Guo & Bin Yang, 2024. "Load Prediction of Regional Heat Exchange Station Based on Fuzzy Clustering Based on Fourier Distance and Convolutional Neural Network–Bidirectional Long Short-Term Memory Network," Energies, MDPI, vol. 17(16), pages 1-19, August.
    10. Ahmad, Tanveer & Chen, Huanxin & Huang, Ronggeng & Yabin, Guo & Wang, Jiangyu & Shair, Jan & Azeem Akram, Hafiz Muhammad & Hassnain Mohsan, Syed Agha & Kazim, Muhammad, 2018. "Supervised based machine learning models for short, medium and long-term energy prediction in distinct building environment," Energy, Elsevier, vol. 158(C), pages 17-32.
    11. Wang, Ran & Lu, Shilei & Feng, Wei, 2020. "A novel improved model for building energy consumption prediction based on model integration," Applied Energy, Elsevier, vol. 262(C).
    12. James Ogundiran & Ehsan Asadi & Manuel Gameiro da Silva, 2024. "A Systematic Review on the Use of AI for Energy Efficiency and Indoor Environmental Quality in Buildings," Sustainability, MDPI, vol. 16(9), pages 1-30, April.
    13. Fan, Cheng & Xiao, Fu & Yan, Chengchu & Liu, Chengliang & Li, Zhengdao & Wang, Jiayuan, 2019. "A novel methodology to explain and evaluate data-driven building energy performance models based on interpretable machine learning," Applied Energy, Elsevier, vol. 235(C), pages 1551-1560.
    14. Li, Guannan & Wu, Yubei & Yoon, Sungmin & Fang, Xi, 2024. "Comprehensive transferability assessment of short-term cross-building-energy prediction using deep adversarial network transfer learning," Energy, Elsevier, vol. 299(C).
    15. Anand Krishnan Prakash & Susu Xu & Ram Rajagopal & Hae Young Noh, 2018. "Robust Building Energy Load Forecasting Using Physically-Based Kernel Models," Energies, MDPI, vol. 11(4), pages 1-21, April.
    16. Haleh Moghaddasi & Charles Culp & Jorge Vanegas & Mehrdad Ehsani, 2021. "Net Zero Energy Buildings: Variations, Clarifications, and Requirements in Response to the Paris Agreement," Energies, MDPI, vol. 14(13), pages 1-21, June.
    17. Vangelis Marinakis, 2020. "Big Data for Energy Management and Energy-Efficient Buildings," Energies, MDPI, vol. 13(7), pages 1-18, March.
    18. Satre-Meloy, Aven, 2019. "Investigating structural and occupant drivers of annual residential electricity consumption using regularization in regression models," Energy, Elsevier, vol. 174(C), pages 148-168.
    19. Joanna Henzel & Łukasz Wróbel & Marcin Fice & Marek Sikora, 2022. "Energy Consumption Forecasting for the Digital-Twin Model of the Building," Energies, MDPI, vol. 15(12), pages 1-21, June.
    20. Cui, Borui & Fan, Cheng & Munk, Jeffrey & Mao, Ning & Xiao, Fu & Dong, Jin & Kuruganti, Teja, 2019. "A hybrid building thermal modeling approach for predicting temperatures in typical, detached, two-story houses," Applied Energy, Elsevier, vol. 236(C), pages 101-116.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:330:y:2025:i:c:s0360544225024764. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.