IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i3p1257-d486897.html
   My bibliography  Save this article

Predicted Medium Vote Thermal Comfort Analysis Applying Energy Simulations with Phase Change Materials for Very Hot-Humid Climates in Social Housing in Ecuador

Author

Listed:
  • Luis Godoy-Vaca

    (Instituto de Investigación Geológico y Energético, Quito 170518, Ecuador)

  • E. Catalina Vallejo-Coral

    (Instituto de Investigación Geológico y Energético, Quito 170518, Ecuador)

  • Javier Martínez-Gómez

    (Instituto de Investigación Geológico y Energético, Quito 170518, Ecuador
    Facultad de Ingeniería y Ciencias Aplicadas, Universidad Internacional SEK, Quito 170302, Ecuador)

  • Marco Orozco

    (Instituto de Investigación Geológico y Energético, Quito 170518, Ecuador)

  • Geovanna Villacreses

    (Instituto de Investigación Geológico y Energético, Quito 170518, Ecuador)

Abstract

This work aims to estimate the expected hours of Predicted Medium Vote (PMV) thermal comfort in Ecuadorian social housing houses applying energy simulations with Phase Change Materials (PCMs) for very hot-humid climates. First, a novel methodology for characterizing three different types of social housing is presented based on a space-time analysis of the electricity consumption in a residential complex. Next, the increase in energy demand under climate influences is analyzed. Moreover, with the goal of enlarging the time of thermal comfort inside the houses, the most suitable PCM for them is determined. This paper includes both simulations and comparisons of thermal behavior by means of the PMV methodology of four types of PCMs selected. From the performed energy simulations, the results show that changing the deck and using RT25-RT30 in walls, it is possible to increase the duration of thermal comfort in at least one of the three analyzed houses. The applied PCM showed 46% of comfortable hours and a reduction of 937 h in which the thermal sensation varies from “very hot” to “hot”. Additionally, the usage time of air conditioning decreases, assuring the thermal comfort for the inhabitants during a higher number of hours per day.

Suggested Citation

  • Luis Godoy-Vaca & E. Catalina Vallejo-Coral & Javier Martínez-Gómez & Marco Orozco & Geovanna Villacreses, 2021. "Predicted Medium Vote Thermal Comfort Analysis Applying Energy Simulations with Phase Change Materials for Very Hot-Humid Climates in Social Housing in Ecuador," Sustainability, MDPI, vol. 13(3), pages 1-31, January.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:3:p:1257-:d:486897
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/3/1257/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/13/3/1257/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Guichard, Stéphane & Miranville, Frédéric & Bigot, Dimitri & Malet-Damour, Bruno & Beddiar, Karim & Boyer, Harry, 2017. "A complex roof incorporating phase change material for improving thermal comfort in a dedicated test cell," Renewable Energy, Elsevier, vol. 101(C), pages 450-461.
    2. Harish, V.S.K.V. & Kumar, Arun, 2016. "A review on modeling and simulation of building energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 1272-1292.
    3. Ang, B.W. & Wang, H. & Ma, Xiaojing, 2017. "Climatic influence on electricity consumption: The case of Singapore and Hong Kong," Energy, Elsevier, vol. 127(C), pages 534-543.
    4. Li, Xiaoma & Zhou, Yuyu & Yu, Sha & Jia, Gensuo & Li, Huidong & Li, Wenliang, 2019. "Urban heat island impacts on building energy consumption: A review of approaches and findings," Energy, Elsevier, vol. 174(C), pages 407-419.
    5. Nafisa Bhikhoo & Arman Hashemi & Heather Cruickshank, 2017. "Improving Thermal Comfort of Low-Income Housing in Thailand through Passive Design Strategies," Sustainability, MDPI, vol. 9(8), pages 1-23, August.
    6. Fonseca, Jimeno A. & Schlueter, Arno, 2015. "Integrated model for characterization of spatiotemporal building energy consumption patterns in neighborhoods and city districts," Applied Energy, Elsevier, vol. 142(C), pages 247-265.
    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. Yuanzheng Li & Wenjing Wang & Yating Wang & Yashu Xin & Tian He & Guosong Zhao, 2020. "A Review of Studies Involving the Effects of Climate Change on the Energy Consumption for Building Heating and Cooling," IJERPH, MDPI, vol. 18(1), pages 1-18, December.
    2. Katal, Ali & Mortezazadeh, Mohammad & Wang, Liangzhu (Leon) & Yu, Haiyi, 2022. "Urban building energy and microclimate modeling – From 3D city generation to dynamic simulations," Energy, Elsevier, vol. 251(C).
    3. Wang, Xiaolu & Tan, Yumin & Zhou, Guanhua & Jing, Guifei & John Francis, Emolu, 2024. "A framework for analyzing energy consumption in urban built-up areas based on single photonic radar and spatial big data," Energy, Elsevier, vol. 290(C).
    4. Schlör, Holger & Venghaus, Sandra & Hake, Jürgen-Friedrich, 2018. "The FEW-Nexus city index – Measuring urban resilience," Applied Energy, Elsevier, vol. 210(C), pages 382-392.
    5. Fei, Wenbin & Bandeira Neto, Luis A. & Dai, Sheng & Cortes, Douglas D. & Narsilio, Guillermo A., 2023. "Numerical analyses of energy screw pile filled with phase change materials," Renewable Energy, Elsevier, vol. 202(C), pages 865-879.
    6. Vallianos, Charalampos & Candanedo, José & Athienitis, Andreas, 2023. "Application of a large smart thermostat dataset for model calibration and Model Predictive Control implementation in the residential sector," Energy, Elsevier, vol. 278(PA).
    7. Waibel, Christoph & Evins, Ralph & Carmeliet, Jan, 2019. "Co-simulation and optimization of building geometry and multi-energy systems: Interdependencies in energy supply, energy demand and solar potentials," Applied Energy, Elsevier, vol. 242(C), pages 1661-1682.
    8. Gao, Datong & Zhao, Bin & Kwan, Trevor Hocksun & Hao, Yong & Pei, Gang, 2022. "The spatial and temporal mismatch phenomenon in solar space heating applications: status and solutions," Applied Energy, Elsevier, vol. 321(C).
    9. Xiao, Lan & Qin, Liang-Liang & Wu, Shuang-Ying, 2023. "Effect of PV-Trombe wall in the multi-storey building on standard effective temperature (SET)-based indoor thermal comfort," Energy, Elsevier, vol. 263(PB).
    10. Yang, Jiangming & Wu, Huijun & Xu, Xinhua & Huang, Gongsheng & Xu, Tao & Guo, Sitong & Liang, Yuying, 2019. "Numerical and experimental study on the thermal performance of aerogel insulating panels for building energy efficiency," Renewable Energy, Elsevier, vol. 138(C), pages 445-457.
    11. Yildiz, B. & Bilbao, J.I. & Sproul, A.B., 2017. "A review and analysis of regression and machine learning models on commercial building electricity load forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 1104-1122.
    12. Zhikun Ding & Rongsheng Liu & Zongjie Li & Cheng Fan, 2020. "A Thematic Network-Based Methodology for the Research Trend Identification in Building Energy Management," Energies, MDPI, vol. 13(18), pages 1-33, September.
    13. Tian, Shen & Shao, Shuangquan & Liu, Bin, 2019. "Investigation on transient energy consumption of cold storages: Modeling and a case study," Energy, Elsevier, vol. 180(C), pages 1-9.
    14. Nutkiewicz, Alex & Yang, Zheng & Jain, Rishee K., 2018. "Data-driven Urban Energy Simulation (DUE-S): A framework for integrating engineering simulation and machine learning methods in a multi-scale urban energy modeling workflow," Applied Energy, Elsevier, vol. 225(C), pages 1176-1189.
    15. Valerie Eveloy & Dereje S. Ayou, 2019. "Sustainable District Cooling Systems: Status, Challenges, and Future Opportunities, with Emphasis on Cooling-Dominated Regions," Energies, MDPI, vol. 12(2), pages 1-64, January.
    16. Minjeong Sim & Dongjun Suh & Marc-Oliver Otto, 2021. "Multi-Objective Particle Swarm Optimization-Based Decision Support Model for Integrating Renewable Energy Systems in a Korean Campus Building," Sustainability, MDPI, vol. 13(15), pages 1-18, August.
    17. Gautham Krishnadas & Aristides Kiprakis, 2020. "A Machine Learning Pipeline for Demand Response Capacity Scheduling," Energies, MDPI, vol. 13(7), pages 1-25, April.
    18. Talebi, Behrang & Haghighat, Fariborz & Tuohy, Paul & Mirzaei, Parham A., 2018. "Validation of a community district energy system model using field measured data," Energy, Elsevier, vol. 144(C), pages 694-706.
    19. Gabriele Battista & Emanuele de Lieto Vollaro & Andrea Vallati & Roberto de Lieto Vollaro, 2023. "Technical–Financial Feasibility Study of a Micro-Cogeneration System in the Buildings in Italy," Energies, MDPI, vol. 16(14), pages 1-15, July.
    20. Sánchez-Guevara Sánchez, Carmen & Sanz Fernández, Ana & Núñez Peiró, Miguel & Gómez Muñoz, Gloria, 2020. "Energy poverty in Madrid: Data exploitation at the city and district level," Energy Policy, Elsevier, vol. 144(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:jsusta:v:13:y:2021:i:3:p:1257-:d:486897. 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.