IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i6p2259-d775045.html
   My bibliography  Save this article

Investigating Optimum Cooling Set Point Temperature and Air Velocity for Thermal Comfort and Energy Conservation in Mixed-Mode Buildings in India

Author

Listed:
  • Sriraj Gokarakonda

    (Energy, Transport and Climate Policy Division, Wuppertal Institute for Climate, Environment and Energy, 42103 Wuppertal, Germany)

  • Christoph van Treeck

    (E3D—Institute of Energy Efficiency and Sustainable Building, RWTH Aachen University, 52074 Aachen, Germany)

  • Rajan Rawal

    (Centre for Advanced Research in Building Science and Energy (CARBSE), CEPT University, Ahmedabad 380009, India)

Abstract

In warm and hot climates, ceiling fans and/or air conditioners (ACs) are used to maintain thermal comfort. Ceiling fans provide air movement near the skin, which enhances the evaporation of sweat, reduces heat stress, and enhances thermal comfort. This is also called the cooling effect. However, AC usage behaviour and the effects of elevated air speed through the use of ceiling fans on indoor operative temperature during AC usage are not widely studied. This study investigated the optimum AC (cooling) set point temperature and air velocity necessary for maintaining thermal comfort while achieving energy conservation, in mixed-mode buildings in India, through field studies by using used custom-built Internet of Things (IOT) devices. In the current study, the results indicate a 79% probability that comfort conditions can be maintained by achieving a temperature drop of 3K. If this drop can be achieved, as much as possible, through passive measures, the duration of AC operation and its energy consumption are reduced, at least by 67.5 and 58.4%, respectively. During the air-conditioned period, there is a possibility that the cooing effect is reduced because of increase in operative temperature due to ceiling fan operation. Therefore, the optimum solution is to maintain the highest AC set point and minimum fan speed setting that are acceptable.

Suggested Citation

  • Sriraj Gokarakonda & Christoph van Treeck & Rajan Rawal, 2022. "Investigating Optimum Cooling Set Point Temperature and Air Velocity for Thermal Comfort and Energy Conservation in Mixed-Mode Buildings in India," Energies, MDPI, vol. 15(6), pages 1-27, March.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:6:p:2259-:d:775045
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/6/2259/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/15/6/2259/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Antonio Paone & Jean-Philippe Bacher, 2018. "The Impact of Building Occupant Behavior on Energy Efficiency and Methods to Influence It: A Review of the State of the Art," Energies, MDPI, vol. 11(4), pages 1-19, April.
    2. Sana Iqbal & Mohammad Sarfraz & Mohammad Ayyub & Mohd Tariq & Ripon K. Chakrabortty & Michael J. Ryan & Basem Alamri, 2021. "A Comprehensive Review on Residential Demand Side Management Strategies in Smart Grid Environment," Sustainability, MDPI, vol. 13(13), pages 1, June.
    3. Hsin-Hung Lin, 2019. "Improvement of Human Thermal Comfort by Optimizing the Airflow Induced by a Ceiling Fan," Sustainability, MDPI, vol. 11(12), pages 1-17, June.
    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. Panagiotis Michailidis & Iakovos Michailidis & Dimitrios Vamvakas & Elias Kosmatopoulos, 2023. "Model-Free HVAC Control in Buildings: A Review," Energies, MDPI, vol. 16(20), pages 1-45, October.
    2. Juana Isabel Méndez & Adán Medina & Pedro Ponce & Therese Peffer & Alan Meier & Arturo Molina, 2022. "Evolving Gamified Smart Communities in Mexico to Save Energy in Communities through Intelligent Interfaces," Energies, MDPI, vol. 15(15), pages 1-29, July.
    3. Abdulrashid Muhammad Kabir & Mohsin Kamal & Fiaz Ahmad & Zahid Ullah & Fahad R. Albogamy & Ghulam Hafeez & Faizan Mehmood, 2021. "Optimized Economic Load Dispatch with Multiple Fuels and Valve-Point Effects Using Hybrid Genetic–Artificial Fish Swarm Algorithm," Sustainability, MDPI, vol. 13(19), pages 1-27, September.
    4. Dániel István Németh & Kálmán Tornai, 2023. "Electrical Load Classification with Open-Set Recognition," Energies, MDPI, vol. 16(2), pages 1-14, January.
    5. Jakob Carlander & Bahram Moshfegh & Jan Akander & Fredrik Karlsson, 2020. "Effects on Energy Demand in an Office Building Considering Location, Orientation, Façade Design and Internal Heat Gains—A Parametric Study," Energies, MDPI, vol. 13(23), pages 1-22, November.
    6. Isabel Andrade & Johann Land & Patricio Gallardo & Susan Krumdieck, 2022. "Application of the InTIME Methodology for the Transition of Office Buildings to Low Carbon—A Case Study," Sustainability, MDPI, vol. 14(19), pages 1-17, September.
    7. Anh Tuan Phan & Thi Tuyet Hong Vu & Dinh Quang Nguyen & Eleonora Riva Sanseverino & Hang Thi-Thuy Le & Van Cong Bui, 2022. "Data Compensation with Gaussian Processes Regression: Application in Smart Building’s Sensor Network," Energies, MDPI, vol. 15(23), pages 1-16, December.
    8. Hyemi Kim & Wonjun Park, 2018. "A Study of the Energy Efficiency Management in Green Standard for Energy and Environmental Design (G-SEED)-Certified Apartments in South Korea," Sustainability, MDPI, vol. 10(10), pages 1-20, September.
    9. Amasyali, Kadir & El-Gohary, Nora M., 2021. "Real data-driven occupant-behavior optimization for reduced energy consumption and improved comfort," Applied Energy, Elsevier, vol. 302(C).
    10. Filipe Soares & André Madureira & Andreu Pagès & António Barbosa & António Coelho & Fernando Cassola & Fernando Ribeiro & João Viana & José Andrade & Marina Dorokhova & Nélson Morais & Nicolas Wyrsch , 2021. "FEEdBACk: An ICT-Based Platform to Increase Energy Efficiency through Buildings’ Consumer Engagement," Energies, MDPI, vol. 14(6), pages 1-43, March.
    11. Amit Shewale & Anil Mokhade & Nitesh Funde & Neeraj Dhanraj Bokde, 2022. "A Survey of Efficient Demand-Side Management Techniques for the Residential Appliance Scheduling Problem in Smart Homes," Energies, MDPI, vol. 15(8), pages 1-34, April.
    12. Baxter Williams & Daniel Bishop & Patricio Gallardo & J. Geoffrey Chase, 2023. "Demand Side Management in Industrial, Commercial, and Residential Sectors: A Review of Constraints and Considerations," Energies, MDPI, vol. 16(13), pages 1-28, July.
    13. Chalal, M.L. & Medjdoub, B. & Bezai, N. & Bull, R. & Zune, M., 2022. "Visualisation in energy eco-feedback systems: A systematic review of good practice," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
    14. Fernando Cassola & Leonel Morgado & António Coelho & Hugo Paredes & António Barbosa & Helga Tavares & Filipe Soares, 2022. "Using Virtual Choreographies to Identify Office Users’ Behaviors to Target Behavior Change Based on Their Potential to Impact Energy Consumption," Energies, MDPI, vol. 15(12), pages 1-21, June.
    15. Georgios Martinopoulos & Anna Serasidou & Panagiota Antoniadou & Agis M. Papadopoulos, 2018. "Building Integrated Shading and Building Applied Photovoltaic System Assessment in the Energy Performance and Thermal Comfort of Office Buildings," Sustainability, MDPI, vol. 10(12), pages 1-24, December.
    16. Diego Casado-Mansilla & Apostolos C. Tsolakis & Cruz E. Borges & Oihane Kamara-Esteban & Stelios Krinidis & Jose Manuel Avila & Dimitrios Tzovaras & Diego López-de-Ipiña, 2020. "Socio-Economic Effect on ICT-Based Persuasive Interventions Towards Energy Efficiency in Tertiary Buildings," Energies, MDPI, vol. 13(7), pages 1-26, April.
    17. Savis Gohari Krangsås & Koen Steemers & Thaleia Konstantinou & Silvia Soutullo & Mingming Liu & Emanuela Giancola & Bahri Prebreza & Touraj Ashrafian & Lina Murauskaitė & Nienke Maas, 2021. "Positive Energy Districts: Identifying Challenges and Interdependencies," Sustainability, MDPI, vol. 13(19), pages 1-20, September.
    18. Seokho Kim & Yujin Song & Yoondong Sung & Donghyun Seo, 2019. "Development of a Consecutive Occupancy Estimation Framework for Improving the Energy Demand Prediction Performance of Building Energy Modeling Tools," Energies, MDPI, vol. 12(3), pages 1-21, January.
    19. 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.
    20. Li, Xinyi & Yao, Runming, 2020. "A machine-learning-based approach to predict residential annual space heating and cooling loads considering occupant behaviour," Energy, Elsevier, vol. 212(C).

    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:gam:jeners:v:15:y:2022:i:6:p:2259-:d:775045. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.