IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v263y2023ipas0360544222025920.html
   My bibliography  Save this article

Adaptive thermal comfort approach to save energy in tropical climate educational building by artificial intelligence

Author

Listed:
  • López-Pérez, Luis Adrián
  • Flores-Prieto, José Jassón

Abstract

This work shows a comparative study of energy savings in an air-conditioning educational building in Aw's tropical climate regarding annual cooling load and degree-days, with adequate comfort levels following the adaptive thermal comfort approach. The comfort temperature modeling was by fuzzy logic-based (FL-BM), artificial neural networks based (ANN-BM), adaptive neuro-fuzzy inference system based (ANFIS-BM) and CIBSE Guide A and a Local linear model, concerning the Mexican standard. In modeling, the mean predicted dissatisfied percentage was 18.1 ± 3.4% and the mean predicted mean vote 0.2 ± 0.08. The ANN-BM was (R2/R2); 24.5 (0.98/0.04) times more accurate than the Local model, 2.9 (0.98/0.34) than FL-BM and 1.7 (0.98/0.57) than ANFIS-BM. The yearly cooling load savings by ANN-BM were 43.7% and 15.6% by the Local model. The ANN-BM annual cooling degree-days showed savings of 33.2% and by Local model 3.2%. ANFIS-BM outputs reduced cooling loads by 15.1% and cooling degree-days by 9.3%. The energy savings appeared when the determined mean Tcomf increases by using more accurate modeling. In air-conditioning buildings in a tropical climate, considering the adaptive approach, the AI-BM allows that Tcomf increase, enabling significant cooling loads reductions and energy savings, providing thermal comfort to the occupants at the same time.

Suggested Citation

  • López-Pérez, Luis Adrián & Flores-Prieto, José Jassón, 2023. "Adaptive thermal comfort approach to save energy in tropical climate educational building by artificial intelligence," Energy, Elsevier, vol. 263(PA).
    • Handle: RePEc:eee:energy:v:263:y:2023:i:pa:s0360544222025920
    DOI: 10.1016/j.energy.2022.125706
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544222025920
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2022.125706?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 search for a different version of it.

    References listed on IDEAS

    as
    1. Annamaria Buonomano, 2016. "Code-to-Code Validation and Application of a Dynamic Simulation Tool for the Building Energy Performance Analysis," Energies, MDPI, vol. 9(4), pages 1-29, April.
    2. Jin Woo Moon & Kyungjae Kim & Hyunsuk Min, 2015. "ANN-Based Prediction and Optimization of Cooling System in Hotel Rooms," Energies, MDPI, vol. 8(10), pages 1-21, September.
    3. Jung, Wooyoung & Jazizadeh, Farrokh, 2020. "Energy saving potentials of integrating personal thermal comfort models for control of building systems: Comprehensive quantification through combinatorial consideration of influential parameters," Applied Energy, Elsevier, vol. 268(C).
    4. Butt, Afaq A. & de Vries, Samuel B. & Loonen, Roel C.G.M. & Hensen, Jan L.M. & Stuiver, Anthonie & van den Ham, Jonathan E.J. & Erich, Bart S.J.F., 2021. "Investigating the energy saving potential of thermochromic coatings on building envelopes," Applied Energy, Elsevier, vol. 291(C).
    5. Jin Woo Moon & Ji-Hyun Lee & Sooyoung Kim, 2014. "Evaluation of Artificial Neural Network-Based Temperature Control for Optimum Operation of Building Envelopes," Energies, MDPI, vol. 7(11), pages 1-21, November.
    6. Harish, V.S.K.V. & Kumar, Arun, 2016. "Reduced order modeling and parameter identification of a building energy system model through an optimization routine," Applied Energy, Elsevier, vol. 162(C), pages 1010-1023.
    7. Ismail, Nagham & Ouahrani, Djamel, 2022. "Modelling of cooling radiant cubicle for an office room to test cooling performance, thermal comfort and energy savings in hot climates," Energy, Elsevier, vol. 244(PB).
    8. Chenari, Behrang & Dias Carrilho, João & Gameiro da Silva, Manuel, 2016. "Towards sustainable, energy-efficient and healthy ventilation strategies in buildings: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 1426-1447.
    9. Bienvenido-Huertas, David & Sánchez-García, Daniel & Rubio-Bellido, Carlos, 2020. "Comparison of energy conservation measures considering adaptive thermal comfort and climate change in existing Mediterranean dwellings," Energy, Elsevier, vol. 190(C).
    10. Shaikh, Pervez Hameed & Nor, Nursyarizal Bin Mohd & Nallagownden, Perumal & Elamvazuthi, Irraivan & Ibrahim, Taib, 2014. "A review on optimized control systems for building energy and comfort management of smart sustainable buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 34(C), pages 409-429.
    11. Chaudhuri, Tanaya & Soh, Yeng Chai & Li, Hua & Xie, Lihua, 2019. "A feedforward neural network based indoor-climate control framework for thermal comfort and energy saving in buildings," Applied Energy, Elsevier, vol. 248(C), pages 44-53.
    12. Cristina Baglivo, 2021. "Dynamic Evaluation of the Effects of Climate Change on the Energy Renovation of a School in a Mediterranean Climate," Sustainability, MDPI, vol. 13(11), pages 1-22, June.
    13. Enescu, Diana, 2017. "A review of thermal comfort models and indicators for indoor environments," Renewable and Sustainable Energy Reviews, Elsevier, vol. 79(C), pages 1353-1379.
    14. Bienvenido-Huertas, David & Sánchez-García, Daniel & Rubio-Bellido, Carlos & Pulido-Arcas, Jesús A., 2021. "Applying the mixed-mode with an adaptive approach to reduce the energy poverty in social dwellings: The case of Spain," Energy, Elsevier, vol. 237(C).
    15. Blázquez, Teresa & Ferrari, Simone & Suárez, Rafael & Sendra, Juan José, 2019. "Adaptive approach-based assessment of a heritage residential complex in southern Spain for improving comfort and energy efficiency through passive strategies: A study based on a monitored flat," Energy, Elsevier, vol. 181(C), pages 504-520.
    16. Nivine Attoue & Isam Shahrour & Rafic Younes, 2018. "Smart Building: Use of the Artificial Neural Network Approach for Indoor Temperature Forecasting," Energies, MDPI, vol. 11(2), pages 1-12, February.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Lee, Minjung & Ham, Jeonggyun & Lee, Jeong-Won & Cho, Honghyun, 2023. "Analysis of thermal comfort, energy consumption, and CO2 reduction of indoor space according to the type of local heating under winter rest conditions," Energy, Elsevier, vol. 268(C).
    2. Fei Xie & Junxue Zhang & Guodong Wu & Chunxia Zhang & Hechi Wang, 2023. "The Environmental Sustainability Study of an Airport Building System Based on an Integrated LCA-Embodied Energy (Emergy)-ANN Analysis," Sustainability, MDPI, vol. 15(9), pages 1-19, May.

    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. Marzullo, Thibault & Keane, Marcus M. & Geron, Marco & Monaghan, Rory F.D., 2019. "A computational toolchain for the automatic generation of multiple Reduced-Order Models from CFD simulations," Energy, Elsevier, vol. 180(C), pages 511-519.
    2. Gianluca Serale & Massimo Fiorentini & Alfonso Capozzoli & Daniele Bernardini & Alberto Bemporad, 2018. "Model Predictive Control (MPC) for Enhancing Building and HVAC System Energy Efficiency: Problem Formulation, Applications and Opportunities," Energies, MDPI, vol. 11(3), pages 1-35, March.
    3. Buyak, Nadia & Deshko, Valeriy & Bilous, Inna & Pavlenko, Anatoliy & Sapunov, Anatoliy & Biriukov, Dmytro, 2023. "Dynamic interdependence of comfortable thermal conditions and energy efficiency increase in a nursery school building for heating and cooling period," Energy, Elsevier, vol. 283(C).
    4. Lee, Minjung & Ham, Jeonggyun & Lee, Jeong-Won & Cho, Honghyun, 2023. "Analysis of thermal comfort, energy consumption, and CO2 reduction of indoor space according to the type of local heating under winter rest conditions," Energy, Elsevier, vol. 268(C).
    5. Clara Ceccolini & Roozbeh Sangi, 2022. "Benchmarking Approaches for Assessing the Performance of Building Control Strategies: A Review," Energies, MDPI, vol. 15(4), pages 1-30, February.
    6. Halhoul Merabet, Ghezlane & Essaaidi, Mohamed & Ben Haddou, Mohamed & Qolomany, Basheer & Qadir, Junaid & Anan, Muhammad & Al-Fuqaha, Ala & Abid, Mohamed Riduan & Benhaddou, Driss, 2021. "Intelligent building control systems for thermal comfort and energy-efficiency: A systematic review of artificial intelligence-assisted techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    7. 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.
    8. Park, June Young & Nagy, Zoltan, 2018. "Comprehensive analysis of the relationship between thermal comfort and building control research - A data-driven literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2664-2679.
    9. Sakiyama, N.R.M. & Carlo, J.C. & Frick, J. & Garrecht, H., 2020. "Perspectives of naturally ventilated buildings: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 130(C).
    10. da Fonseca, André L.A. & Chvatal, Karin M.S. & Fernandes, Ricardo A.S., 2021. "Thermal comfort maintenance in demand response programs: A critical review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 141(C).
    11. Barone, G. & Buonomano, A. & Forzano, C. & Giuzio, G.F. & Palombo, A. & Russo, G., 2023. "A new thermal comfort model based on physiological parameters for the smart design and control of energy-efficient HVAC systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 173(C).
    12. Paulína Šujanová & Monika Rychtáriková & Tiago Sotto Mayor & Affan Hyder, 2019. "A Healthy, Energy-Efficient and Comfortable Indoor Environment, a Review," Energies, MDPI, vol. 12(8), pages 1-37, April.
    13. Savadkoohi, Marjan & Macarulla, Marcel & Casals, Miquel, 2023. "Facilitating the implementation of neural network-based predictive control to optimize building heating operation," Energy, Elsevier, vol. 263(PB).
    14. Zhan, Sicheng & Chong, Adrian, 2021. "Data requirements and performance evaluation of model predictive control in buildings: A modeling perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 142(C).
    15. D’Oca, Simona & Hong, Tianzhen & Langevin, Jared, 2018. "The human dimensions of energy use in buildings: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 731-742.
    16. Refaa Sokkar & Halil Z. Alibaba, 2020. "Thermal Comfort Improvement for Atrium Building with Double-Skin Skylight in the Mediterranean Climate," Sustainability, MDPI, vol. 12(6), pages 1-26, March.
    17. Ma, Nan & Aviv, Dorit & Guo, Hongshan & Braham, William W., 2021. "Measuring the right factors: A review of variables and models for thermal comfort and indoor air quality," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    18. Martín Pensado-Mariño & Lara Febrero-Garrido & Pablo Eguía-Oller & Enrique Granada-Álvarez, 2021. "Feasibility of Different Weather Data Sources Applied to Building Indoor Temperature Estimation Using LSTM Neural Networks," Sustainability, MDPI, vol. 13(24), pages 1-15, December.
    19. Mukhtar, A. & Ng, K.C. & Yusoff, M.Z., 2018. "Passive thermal performance prediction and multi-objective optimization of naturally-ventilated underground shelter in Malaysia," Renewable Energy, Elsevier, vol. 123(C), pages 342-352.
    20. David Vérez & Luisa F. Cabeza, 2021. "Which Building Services Are Considered to Have Impact on Climate Change?," Energies, MDPI, vol. 14(13), pages 1-16, June.

    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:263:y:2023:i:pa:s0360544222025920. 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.