Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Advertisement

Scientific Reports
  • View all journals
  • Search
  • My Account Login
  • Content Explore content
  • About the journal
  • Publish with us
  • Sign up for alerts
  • RSS feed
  1. nature
  2. scientific reports
  3. articles
  4. article
An improved African vulture optimization algorithm for energy comfort management in occupancy driven smart buildings
Download PDF
Download PDF
  • Article
  • Open access
  • Published: 08 January 2026

An improved African vulture optimization algorithm for energy comfort management in occupancy driven smart buildings

  • Ghulam Fizza1,6 na1,
  • Kushsairy Kadir1 na1,
  • Haidawati Nasir2 na1,
  • Muhammad Islam3,
  • Sheroz Khan4 na1 &
  • …
  • Mohammad Rashid5 na1 

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

  • 660 Accesses

  • Metrics details

We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Subjects

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

Abstract

Smart buildings enabled by sensing and control technologies offer opportunities to optimize indoor environmental quality (IEQ) while reducing energy consumption. However, most existing optimization frameworks neglect occupancy dynamics which leads to unnecessary energy consumption and inconsistent comfort. This study proposed a Multi-Trial Vector based African Vulture Optimization Algorithm (MTV-AVOA) with occupancy driven constraint handling mechanism, designed to jointly optimize temperature, humidity, illumination, and air quality. A 528-hour smart office dataset was used to benchmark MTV-AVOA against four state of the art algorithms. The obtained results show that MTV-AVOA delivers average comfort index value during occupied hours is 0.8026. while during non-occupied hours it is 0.750. It also significantly improves from the non-optimized case (0.665) and yields the most balanced overall outcome. The proposed algorithm achieves lower energy consumption during both occupied (638.10 kWh) and non-occupied (528.20 kWh) compared to benchmark algorithms. These evaluation demonstrate that by linking occupancy with IEQ optimization, MTV-AVOA offers an effective balance between comfort and energy efficiency than existing approaches, thus contributing to the development of sustainable smart buildings.

Similar content being viewed by others

Dataset on occupant behavior, indoor environment, and energy use before and after dormitory retrofit

Article Open access 15 May 2025

Energy consumption and IEQ monitoring in two university apartment buildings: Pre-retrofit dataset

Article Open access 18 June 2025

Parameterising the effect of human occupancy and kinetic energy on indoor air pollution

Article Open access 14 January 2026

Data availability

The datasets analysed in this study are publicly available in the SoBigData Services and Products repository at https://ckan-sobigdata.d4science.org/dataset/multi-sensor_dataset_of_environmental_conditions_in_smart_office (accessed 4 December 2024).

References

  1. Niza, I. L., Gomes, G. C. C. & Broday, E. E. Indoor environmental quality models: A bibliometric, mapping and clustering review. Renew. Sustain. Energy Rev. 203, 114791. https://doi.org/10.1016/j.rser.2024.114791 (2024).

    Google Scholar 

  2. Roumi, S., Zhang, F., Stewart, R. A. & Santamouris, M. Indoor environment quality effects on occupant satisfaction and energy consumption: Empirical evidence from subtropical offices. Energy Build. 303, 113784. https://doi.org/10.1016/j.enbuild.2023.113784 (2024).

    Google Scholar 

  3. Elhami, M., Goodarzi, S. S., Maleki, S. & Sajadi, B. Three-objective optimization of the HVAC system control strategy in an educational building to reduce energy consumption and enhance indoor environmental quality (IEQ) using machine learning techniques. J. Build. Eng. 105, 112444. https://doi.org/10.1016/j.jobe.2025.112444 (2025).

    Google Scholar 

  4. Braga, M. P., Niza, I. L. & Broday, E. E. Assessment between indoor environmental quality aspects and productivity in buildings: A systematic literature review. Build. Environ. 278, 112983. https://doi.org/10.1016/j.buildenv.2025.112983 (2025).

    Google Scholar 

  5. Felgueiras, F., Mourão, Z., Moreira, A. & Gabriel, M. F. Multi-domain indoor environmental quality and worker health, well-being, and productivity: Objective and subjective assessments in modern office buildings. Build. Environ. 282, 113320. https://doi.org/10.1016/j.buildenv.2025.113320 (2025).

    Google Scholar 

  6. Perera, I., Hewage, K., Rana, A. & Sadiq, R. Combining Energy Performance and Indoor Environmental Quality (IEQ) in Buildings: A Systematic Review on Common IEQ Guidelines and Energy Codes in North America. Energies 18(7), 1740. https://doi.org/10.3390/en18071740 (2025).

    Google Scholar 

  7. Shah, A. S., Nasir, H., Fayaz, M., Lajis, A. & Shah, A. A Review on Energy Consumption Optimization Techniques in IoT Based Smart Building Environments. Information 10(3), 108. https://doi.org/10.3390/info10030108 (2019).

    Google Scholar 

  8. Jones ER, Cedeño Laurent JG, Young AS, Coull BA, Spengler JD, Glen J. Indoor humidity levels and associations with reported symptoms in office buildings. Indoor Air, 2022, https://doi.org/10.1111/ina.12961.

  9. Rastogi, D. et al. The role of humidity in determining future electricity demand in the southeastern United States. Environ. Res. Lett. 16(11), 114017. https://doi.org/10.1088/1748-9326/ac2fdf (2021).

    Google Scholar 

  10. American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE). 2021 ASHRAE Handbook: Fundamentals. Available from: https://www.ashrae.org/technical-resources/ashrae-handbook/description-2021-ashrae-handbook-fundamentals.

  11. Wolkoff, P. Indoor air humidity, air quality, and health - An overview. Int. J. Hyg. Environ. Health 221(3), 376–390. https://doi.org/10.1016/j.ijheh.2018.01.015 (2018).

    Google Scholar 

  12. Salimi, S. & Hammad, A. Optimizing energy consumption and occupants comfort in open-plan offices using local control based on occupancy dynamic data. Build. Environ. 176, 106818. https://doi.org/10.1016/j.buildenv.2020.106818 (2020).

    Google Scholar 

  13. Yuan, Y., Song, C., Gao, L., Zeng, K. & Chen, Y. A review of current research on occupant-centric control for improving comfort and energy efficiency. Build. Simul. 17(10), 1675–1692. https://doi.org/10.1007/s12273-024-1170-1 (2024).

    Google Scholar 

  14. Wu, Z. et al. Adaptation-based indoor environment control with night natural ventilation in autumn in an office building in a hot-humid area. Build. Environ. 243, 110702. https://doi.org/10.1016/j.buildenv.2023.110702 (2023).

    Google Scholar 

  15. Wang, D., Chen, Y., Wang, W., Gao, C. & Wang, Z. Field test of Model Predictive Control in residential buildings for utility cost savings. Energy and Buildings 288, 113026. https://doi.org/10.1016/j.enbuild.2023.113026 (2023).

    Google Scholar 

  16. Hou, F. et al. Occupancy-driven HVAC control optimization via LSTM and deep reinforcement learning for enhanced indoor air quality, thermal comfort and energy efficiency. Build. Environ.284, 113501. https://doi.org/10.1016/j.buildenv.2025.113501 (2025).

    Google Scholar 

  17. Liu J, Wu B, Meng X, Wu J, Ma Z. LearnAMR: Learning-based adaptive model predictive control enhanced by reinforcement learning for optimizing energy flexibility in building energy systems incorporating demand-side management Applied Energy, 2025, 401: 126707. https://doi.org/10.1016/j.apenergy.2025.126707.

  18. Ala’raj, M., Radi, M., Abbod, M. F., Majdalawieh, M. & Parodi, M. Data-driven based HVAC optimization approaches: A Systematic Literature Review. J. Build. Eng. 46, 103678. https://doi.org/10.1016/j.jobe.2021.103678 (2022).

    Google Scholar 

  19. Al Mindeel, T., Spentzou, E. & Eftekhari, M. Energy, thermal comfort, and indoor air quality: Multi-objective optimization review. Renew. Sustain. Energy Rev. 202, 114682. https://doi.org/10.1016/j.rser.2024.114682 (2024).

    Google Scholar 

  20. Abdollahzadeh, B., Soleimanian Gharehchopogh, F. & Mirjalili, S. African vultures optimization algorithm: A new nature-inspired metaheuristic algorithm for global optimization problems. Comput. Ind. Eng. 158, 107408. https://doi.org/10.1016/j.cie.2021.107408 (2021).

    Google Scholar 

  21. Zheng, R. et al. A multi-strategy enhanced African vultures optimization algorithm for global optimization problems. J. Comput. Design Eng. 10(1), 329–356. https://doi.org/10.1093/jcde/qwac135 (2023).

    Google Scholar 

  22. Xu, R. et al. A decentralized federated learning based interoperable and heterogeneity aware predictive optimization method for energy and comfort in smart homes environment. Appl. Soft Comput. 161, 111689. https://doi.org/10.1016/j.asoc.2024.111689 (2024).

    Google Scholar 

  23. Khan, Q. W. et al. Optimizing energy efficiency and comfort in smart homes through predictive optimization: A case study with indoor environmental parameter consideration. Energy Rep. 11, 5619–5637. https://doi.org/10.1016/j.egyr.2024.05.038 (2024).

    Google Scholar 

  24. Khan, A. N., Rizwan, A., Ahmad, R. & Kim, D. H. An OCF-IoTivity enabled smart-home optimal indoor environment control system for energy and comfort optimization. Internet Things 22, 100712. https://doi.org/10.1016/j.iot.2023.100712 (2023).

    Google Scholar 

  25. Shah, A. S. et al. Dynamic user preference parameters selection and energy consumption optimization for smart homes using deep extreme learning machine and bat algorithm. IEEE Access 8, 204744–204762. https://doi.org/10.1109/ACCESS.2020.3037081 (2020).

    Google Scholar 

  26. Malik, S., Shafqat, W., Lee, K. T. & Kim, D. H. A feature selection-based predictive-learning framework for optimal actuator control in smart homes. Actuators 10(4), 84. https://doi.org/10.3390/act10040084 (2021).

    Google Scholar 

  27. García Ruiz, A. H. et al. Assessing a multi-objective genetic algorithm with a simulated environment for energy-saving of air conditioning systems with user preferences. Symmetry 13(2), 344. https://doi.org/10.3390/sym13020344 (2021).

  28. Verma, A., Pandey, R. K. & Kumar, M. AI-based building management and information system with multi-agent topology for energy-efficient buildings: Towards occupants’ comfort. Int. J. Intell. Eng. Syst. 13(6), 230–241. https://doi.org/10.1080/03772063.2020.1847701 (2020).

    Google Scholar 

  29. Verma A, Shukla S, Sharma R. A novel design approach for indoor environmental quality based on a multi-agent system for intelligent buildings in a smart city: Towards occupant’s comfort. Int. J. Smart Grid Clean Energy 11(2): 120–131. https://doi.org/10.12720/sgce.11.2.120-131 (2022).

  30. Verma, A., Prakash, S., Srivastava, V., Kumar, A. & Mukhopadhyay, S. C. Sensing, controlling, and IoT infrastructure in smart building: A review. IEEE Sens. J. 19(20), 9036–9046. https://doi.org/10.1109/JSEN.2019.2922409 (2019).

    Google Scholar 

  31. Gaur, A., Singh, A., Verma, A. & Kumar, A. Artificial intelligence and multi-sensor fusion based universal fire detection system for smart buildings using IoT techniques. IETE J. Res. 69(12), 9204–9216. https://doi.org/10.1080/03772063.2022.2088626 (2023).

    Google Scholar 

  32. Verma, A., Prakash, S. & Kumar, A. A comparative analysis of data-driven based optimization models for energy-efficient buildings. IETE J. Res. 69(2), 796–812. https://doi.org/10.1080/03772063.2020.1838347 (2023).

    Google Scholar 

  33. Verma, A., Prakash, S. & Kumar, A. ANN-based energy consumption prediction model up to 2050 for a residential building: Towards sustainable decision making. Environ. Progr. Sustain. Energy 40(3), e13544. https://doi.org/10.1002/ep.13544 (2021).

    Google Scholar 

  34. Maurya P, Verma A, Gupta R. Python-based optimization and comparative analysis of temperature and PM2.5 environmental parameters for energy efficient buildings. IJARESM (International Journal of All Research Education and Scientific Methods), 2022, 10(9): 1425–1431. Available from: https://www.ijaresm.com/uploaded_files/document_file/Palak-Maurya-_2_C0rY.pdf.

  35. Maurya P, Verma A, Gupta R, Sahay K. Optimization of environmental parameters (relative humidity) and IAQ (PM10) for residential buildings using enhanced JAYA algorithm. In: 2023 Second International Conference on Augmented Intelligence and Sustainable Systems (ICAISS). 2023: 1721–1725. https://doi.org/10.1109/ICAISS58487.2023.10250471.

  36. Fayaz, M. & Kim, D. H. Energy consumption optimization and user comfort management in residential buildings using a bat algorithm and fuzzy logic. Energies 11(1), 161. https://doi.org/10.3390/en11010161 (2018).

    Google Scholar 

  37. Wahid F, Fayaz M, Aljarbouh A, Mir M, Aamir M, Imran. Energy consumption optimization and user comfort maximization in smart buildings using a hybrid of the firefly and genetic algorithms. Energies, 2020, 13(17): 4363. https://doi.org/10.3390/en13174363.

  38. Malik, S., Lee, K. T. & Kim, D. H. Optimal control based on scheduling for comfortable smart home environment. IEEE Access 8, 218245–218256. https://doi.org/10.1109/ACCESS.2020.3042534 (2020).

    Google Scholar 

  39. Ullah, I. & Kim, D. An improved optimization function for maximizing user comfort with minimum energy consumption in smart homes. Energies 10(11), 1818. https://doi.org/10.3390/en10111818 (2017).

    Google Scholar 

  40. Abdul Malek, M. R. et al. Comfort and energy consumption optimization in smart homes using bat algorithm with inertia weight. J. Build. Eng. 47, 103848. https://doi.org/10.1016/j.jobe.2021.103848 (2022).

    Google Scholar 

  41. Baharudin, F. N. A., Nor, N. A., Abdul Malek, M. R., Ghazali, A. K. & Ibrahim, Z. Indoor comfort and energy consumption optimization using an inertia weight artificial bee colony algorithm. Algorithms 15(11), 395. https://doi.org/10.3390/a15110395 (2022).

  42. Sulaiman, M. H. & Mustaffa, Z. Using the evolutionary mating algorithm for optimizing the user comfort and energy consumption in smart building. J. Build. Eng. 76, 107139. https://doi.org/10.1016/j.jobe.2023.107139 (2023).

    Google Scholar 

  43. Khan, A. N. et al. Dynamic temporal analysis and modeling of residential lighting consumption for energy efficiency and sustainability. IEEE Access 12, 154365–154380. https://doi.org/10.1109/ACCESS.2024.3467337 (2024).

    Google Scholar 

  44. Jin, W., Ullah, I., Ahmad, S. & Kim, D. Occupant comfort management based on energy optimization using an environment prediction model in smart homes. Sustainability 11(4), 997. https://doi.org/10.3390/su11040997 (2019).

    Google Scholar 

  45. Minassian, R., Mihăiktă, A. S. & Shirazi, A. Optimizing indoor environmental prediction in smart buildings: A comparative analysis of deep learning models. Energy Build 327, 115086. https://doi.org/10.1016/j.enbuild.2024.115086 (2025).

    Google Scholar 

  46. Liu, Y., Dang, R., Yang, B. & Liu, P. Energy-efficient control strategy for air conditioning and mechanical ventilation system based on occupant distribution-A case study on stratum ventilation. J. Build. Eng. 100, 111709. https://doi.org/10.1016/j.jobe.2024.111709 (2025).

    Google Scholar 

  47. Ono, E., Chong, A. & Lam, K. P. Occupant-centric zoning design framework for flexible workplaces: Impact of occupancy modeling and building operation. Build. Environ. 267, 112294. https://doi.org/10.1016/j.buildenv.2024.112294 (2025).

    Google Scholar 

  48. Zhang, S., Su, H., Wu, M. & Yang, B. An empirical study on energy usage and occupant comfort of occupant-centric control systems in offices. Build. Environ. 278, 112954. https://doi.org/10.1016/j.buildenv.2025.112954 (2025).

    Google Scholar 

  49. Sun, K., Qaisar, I., Khan, M. A., Xing, T. & Zhao, Q. Building occupancy number prediction: A Transformer approach. Build. Environ. 244, 110807. https://doi.org/10.1016/j.buildenv.2023.110807 (2023).

    Google Scholar 

  50. Qaisar, I., Liang, W., Sun, K., Xing, T. & Zhao, Q. An experimental comparative study of energy saving based on occupancy-centric control in smart buildings. Build. Environ. 268, 112322. https://doi.org/10.1016/j.buildenv.2024.112322 (2025).

    Google Scholar 

  51. Qaisar, I., Sun, K. & Zhao, Q. Exploring large language models for indoor occupancy measurement in smart office buildings. Build. Environ. 287, 113860. https://doi.org/10.1016/j.buildenv.2025.113860 (2026).

    Google Scholar 

  52. Nadimi-Shahraki, M. H., Farhanginasab, H., Taghian, S., Sadiq, A. S. & Mirjalili, S. Multi-trial vector-based whale optimization algorithm. J. Bionic Eng. 21(3), 1465–1495. https://doi.org/10.1007/s42235-024-00493-8 (2024).

    Google Scholar 

  53. CKAN Smart Office Dataset. Multi-sensor Dataset of Environmental Conditions in Smart Office. 2024. Available from: https://ckan-sobigdata.d4science.org/dataset/multi-sensor_dataset_of_environmental_conditions_in_smart_office. Accessed on 4 December 2024.

  54. Khan, I. et al. Leveraging distributed AI for multi-occupancy prediction in cognitive buildings. Internet of Things 26, 101181. https://doi.org/10.1016/j.iot.2024.101181 (2024).

    Google Scholar 

  55. Li, Y. et al. Sensor incipient fault impacts on building energy performance: A case study on a multi-zone commercial building. Buildings 13(2), 520. https://doi.org/10.3390/buildings13020520 (2023).

    Google Scholar 

  56. Jena, B., Naik, M. K., Panda, R. & Abraham, A. A novel minimum generalized cross entropy-based multilevel segmentation technique for the brain MRI/dermoscopic images. Comput. Biol. Med. 151, 106214. https://doi.org/10.1016/j.compbiomed.2022.106214 (2022).

    Google Scholar 

  57. Nadimi-Shahraki, M. H., Taghian, S., Mirjalili, S. & Faris, H. MTDE: An effective multi-trial vector-based differential evolution algorithm and its applications for engineering design problems. Appl. Soft Comput. 97, 106761. https://doi.org/10.1016/j.asoc.2020.106761 (2020).

    Google Scholar 

  58. Nadimi-Shahraki, M. H. et al. MTV-SCA: Multi-trial vector-based sine cosine algorithm. Clust. Comput. https://doi.org/10.1007/s10586-024-04602-4 (2024).

    Google Scholar 

  59. Fizza G, Kadir K, Nasir H. Optimizing synthesized comfort index through occupancy driven in IoT based smart buildings. In: Proceedings of the 2023 International Conference on Engineering Technology and Technopreneurship (ICE2T), Kuala Lumpur, Malaysia, 2024: 130–135. IEEE. https://doi.org/10.1109/ICE2T58637.2023.10540535.

  60. Wolkoff, P., Azuma, K. & Carrer, P. Health, work performance, and risk of infection in office-like environments: The role of indoor temperature, air humidity, and ventilation. Int. J. Hyg. Environ. Health 233, 113709. https://doi.org/10.1016/j.ijheh.2021.113709 (2021).

    Google Scholar 

  61. Campano Má, Acosta I, Domínguez S, López-Lovillo R. Dynamic analysis of office lighting smart controls management based on user requirements. Autom. Constr. 133: 104021. https://doi.org/10.1016/j.autcon.2021.104021 (2022).

Download references

Acknowledgements

The researchers would like to thank the Deanship of Graduate Studies and Scientific Research at Qassim University for financial support (QU-APC-2025). UniKL supports for the research of this work from the Center for Research and Innovation (CoRI), Universiti Kuala Lumpur (UniKL) under the UniKL Excellent Research Grant Scheme, Grant Code: UniKL/CoRI/UER22002. Additionally, the authors acknowledge the computational resources provided through the UniKL Excellent Research Grant Scheme and access to the Intelligent Embedded Research Lab (IERL) at UniKL BMI.

Funding

The researchers would like to thank the Deanship of Graduate Studies and Scientific Research at Qassim University for financial support (QU-APC-2025).

Author information

Author notes

  1. Ghulam Fizza, Kushsairy Kadir, Haidawati Nasir, Sheroz Khan and Mohammad Rashid have contributed equally to this work.

Authors and Affiliations

  1. Department of Electrical and Electronic Engineering, Universiti Kuala Lumpur British Malaysian Institute (UniKL BMI), 53100, Selangor, Malaysia

    Ghulam Fizza & Kushsairy Kadir

  2. Department of Computer Engineering, Universiti Kuala Lumpur Malaysian Institute of Information Technology (UniKL MIIT), 50480, Kuala Lumpur, Malaysia

    Haidawati Nasir

  3. Department of Electrical Engineering, College of Engineering, Qassim University, 52571, Buraydah, Saudi Arabia

    Muhammad Islam

  4. Department of Electrical Engineering, College of Engineering and Information Technology, Onaizah Colleges, 56447, Onaizah, Saudi Arabia

    Sheroz Khan

  5. Wireless Communication Center, Faculty of Electrical Engineering, Universiti Teknologi Malaysia (UTM), 81310, Skudai, Johor, Malaysia

    Mohammad Rashid

  6. Department of Telecommunication Engineering, Quaid-e-Awam University of Engineering, Science and Technology, 67480, Nawabshah, Sindh, Pakistan

    Ghulam Fizza

Authors
  1. Ghulam Fizza
    View author publications

    Search author on:PubMed Google Scholar

  2. Kushsairy Kadir
    View author publications

    Search author on:PubMed Google Scholar

  3. Haidawati Nasir
    View author publications

    Search author on:PubMed Google Scholar

  4. Muhammad Islam
    View author publications

    Search author on:PubMed Google Scholar

  5. Sheroz Khan
    View author publications

    Search author on:PubMed Google Scholar

  6. Mohammad Rashid
    View author publications

    Search author on:PubMed Google Scholar

Contributions

Conceptualization, G.F., K.K. and H.N.; methodology, G.F.; software, G.F.; validation, G.F. and H.N.; formal analysis, S.K., M.I. and M.R.; investigation, G.F. and M.R.; resources, K.K., S.K., M.I. and H.N.; data curation, G.F., K.K. and H.N.; writing—original draft preparation, G.F.; writing—review and editing, K.K., H.N., M.I. and S.K.; visualization, K.K.and H.N., M.R.; supervision, H.N. and K.K.; project administration, K.K. and H.N.; funding acquisition, K.K., M.I. and S.K. All authors have read and agreed to the published version of the manuscript.

Corresponding author

Correspondence to Muhammad Islam.

Ethics declarations

Competing interests

The authors declare no competing interests.

Ethics approval

We (the authors) assure that we are committed to Ethics provided by the journal.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Supplementary Information.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fizza, G., Kadir, K., Nasir, H. et al. An improved African vulture optimization algorithm for energy comfort management in occupancy driven smart buildings. Sci Rep (2026). https://doi.org/10.1038/s41598-025-34440-2

Download citation

  • Received: 22 September 2025

  • Accepted: 29 December 2025

  • Published: 08 January 2026

  • DOI: https://doi.org/10.1038/s41598-025-34440-2

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Keywords

  • Smart building
  • Occupancy
  • Comfort index
  • Energy consumption
  • Indoor environmental quality
  • Multi trial vector african vulture optimization algorithm (MTV-AVOA)
Download PDF

Advertisement

Explore content

  • Research articles
  • News & Comment
  • Collections
  • Subjects
  • Follow us on Facebook
  • Follow us on Twitter
  • Sign up for alerts
  • RSS feed

About the journal

  • About Scientific Reports
  • Contact
  • Journal policies
  • Guide to referees
  • Calls for Papers
  • Editor's Choice
  • Journal highlights
  • Open Access Fees and Funding

Publish with us

  • For authors
  • Language editing services
  • Open access funding
  • Submit manuscript

Search

Advanced search

Quick links

  • Explore articles by subject
  • Find a job
  • Guide to authors
  • Editorial policies

Scientific Reports (Sci Rep)

ISSN 2045-2322 (online)

nature.com sitemap

About Nature Portfolio

  • About us
  • Press releases
  • Press office
  • Contact us

Discover content

  • Journals A-Z
  • Articles by subject
  • protocols.io
  • Nature Index

Publishing policies

  • Nature portfolio policies
  • Open access

Author & Researcher services

  • Reprints & permissions
  • Research data
  • Language editing
  • Scientific editing
  • Nature Masterclasses
  • Research Solutions

Libraries & institutions

  • Librarian service & tools
  • Librarian portal
  • Open research
  • Recommend to library

Advertising & partnerships

  • Advertising
  • Partnerships & Services
  • Media kits
  • Branded content

Professional development

  • Nature Awards
  • Nature Careers
  • Nature Conferences

Regional websites

  • Nature Africa
  • Nature China
  • Nature India
  • Nature Japan
  • Nature Middle East
  • Privacy Policy
  • Use of cookies
  • Legal notice
  • Accessibility statement
  • Terms & Conditions
  • Your US state privacy rights
Springer Nature

© 2026 Springer Nature Limited

Nature Briefing Anthropocene

Sign up for the Nature Briefing: Anthropocene newsletter — what matters in anthropocene research, free to your inbox weekly.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing: Anthropocene