IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i22p9770-d1517169.html
   My bibliography  Save this article

Determine the Profiles of Power Consumption in Commercial Buildings in a Very Hot Humid Climate Using a Temporary Series

Author

Listed:
  • E. Catalina Vallejo-Coral

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

  • Ricardo Garzón

    (Departamento de Ingeniería Mecánica, Escuela Politécnica Nacional, Ladrón de Guevara E11-253, Quito 170517, Ecuador)

  • Miguel Darío Ortega López

    (Departamento de Ingeniería Mecánica, Escuela Politécnica Nacional, Ladrón de Guevara E11-253, Quito 170517, Ecuador)

  • Javier Martínez-Gómez

    (Universidad de Alcalá, Escuela Politécnica, Departamento de Teoría de la Señal y Comunicación, (Área de Ingeniería Mecánica), 28805 Alcalá de Henares, Spain
    Facultad de Ingeniería y Ciencias Aplicadas, Universidad Internacional SEK, Albert Einstein s/n and 5th, Quito 170302, Ecuador)

  • Marcelo Moya

    (Facultad de Ciencias Técnicas, Universidad Internacional Del Ecuador UIDE, Quito 170411, Ecuador)

Abstract

With the growth of the nations, the commercial and public services sectors have recently seen an increase in their electricity usage. This demonstrates how crucial it is to understand a building’s behavior in order to lower its usage. This requires on-site data collection by qualified professionals and specialized equipment, which represents high costs. However, multiple studies have demonstrated that it is possible to find electricity-saving strategies from the study of electricity usage, recorded in an hourly period or less, captured by smart meters. In this context, the present study applies a methodology to determine useful information on the operation and characteristics of public buildings on the Ecuadorian coast based on the data gathered over a period of five consecutive months from smart meters. The methodology consists of four steps: (1) data cleaning and filling, (2) time-series decomposition, (3) the generation of consumption profile and (4) the identification of the temperature influence. According to the results, the pre-cooling of spaces accounts for 5% of all electricity used in the commercial buildings, while prolonged shutdown uses 10%. Approximately USD 1100 per month would be spent on the main building and USD 78 on the agency as a result.

Suggested Citation

  • E. Catalina Vallejo-Coral & Ricardo Garzón & Miguel Darío Ortega López & Javier Martínez-Gómez & Marcelo Moya, 2024. "Determine the Profiles of Power Consumption in Commercial Buildings in a Very Hot Humid Climate Using a Temporary Series," Sustainability, MDPI, vol. 16(22), pages 1-18, November.
  • Handle: RePEc:gam:jsusta:v:16:y:2024:i:22:p:9770-:d:1517169
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/22/9770/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/16/22/9770/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jaqueline Litardo & Ruben Hidalgo-Leon & Guillermo Soriano, 2021. "Energy Performance and Benchmarking for University Classrooms in Hot and Humid Climates," Energies, MDPI, vol. 14(21), pages 1-17, October.
    2. Shen, Bo & Price, Lynn & Lu, Hongyou, 2012. "Energy audit practices in China: National and local experiences and issues," Energy Policy, Elsevier, vol. 46(C), pages 346-358.
    3. Ethan M Pickering & Mohammad A Hossain & Jack P Mousseau & Rachel A Swanson & Roger H French & Alexis R Abramson, 2017. "A cross-sectional study of the temporal evolution of electricity consumption of six commercial buildings," PLOS ONE, Public Library of Science, vol. 12(10), pages 1-27, October.
    4. Li, Kehua & Ma, Zhenjun & Robinson, Duane & Ma, Jun, 2018. "Identification of typical building daily electricity usage profiles using Gaussian mixture model-based clustering and hierarchical clustering," Applied Energy, Elsevier, vol. 231(C), pages 331-342.
    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. Liu, Benxi & Liu, Tengyuan & Liao, Shengli & Lu, Jia & Cheng, Chuntian, 2023. "Short-term coordinated hybrid hydro-wind-solar optimal scheduling model considering multistage section restrictions," Renewable Energy, Elsevier, vol. 217(C).
    2. Kong, Lingbo & Price, Lynn & Hasanbeigi, Ali & Liu, Huanbin & Li, Jigeng, 2013. "Potential for reducing paper mill energy use and carbon dioxide emissions through plant-wide energy audits: A case study in China," Applied Energy, Elsevier, vol. 102(C), pages 1334-1342.
    3. Csereklyei, Zsuzsanna & Anantharama, Nandini & Kallies, Anne, 2021. "Electricity market transitions in Australia: Evidence using model-based clustering," Energy Economics, Elsevier, vol. 103(C).
    4. Do-Hyeon Ryu & Ryu-Hee Kim & Seung-Hyun Choi & Kwang-Jae Kim & Young Myoung Ko & Young-Jin Kim & Minseok Song & Dong Gu Choi, 2020. "Utilizing Electricity Consumption Data to Assess the Noise Discomfort Caused by Electrical Appliances between Neighbors: A Case Study of a Campus Apartment Building," Sustainability, MDPI, vol. 12(20), pages 1-16, October.
    5. Perroni, Marcos G. & Gouvea da Costa, Sergio E. & Pinheiro de Lima, Edson & Vieira da Silva, Wesley & Tortato, Ubiratã, 2018. "Measuring energy performance: A process based approach," Applied Energy, Elsevier, vol. 222(C), pages 540-553.
    6. Zhao, Xiaofan & Li, Huimin & Wu, Liang & Qi, Ye, 2014. "Implementation of energy-saving policies in China: How local governments assisted industrial enterprises in achieving energy-saving targets," Energy Policy, Elsevier, vol. 66(C), pages 170-184.
    7. Grillone, Benedetto & Mor, Gerard & Danov, Stoyan & Cipriano, Jordi & Sumper, Andreas, 2021. "A data-driven methodology for enhanced measurement and verification of energy efficiency savings in commercial buildings," Applied Energy, Elsevier, vol. 301(C).
    8. Yelena Petrenko & Igor Denisov & Gaukhar Koshebayeva & Valeriy Biryukov, 2020. "Energy Efficiency of Kazakhstan Enterprises: Unexpected Findings," Energies, MDPI, vol. 13(5), pages 1-20, February.
    9. Wang, Zhenfeng & Xu, Guangyin & Lin, Ruojue & Wang, Heng & Ren, Jingzheng, 2019. "Energy performance contracting, risk factors, and policy implications: Identification and analysis of risks based on the best-worst network method," Energy, Elsevier, vol. 170(C), pages 1-13.
    10. Katarina Bäcklund & Marco Molinari & Per Lundqvist & Björn Palm, 2023. "Building Occupants, Their Behavior and the Resulting Impact on Energy Use in Campus Buildings: A Literature Review with Focus on Smart Building Systems," Energies, MDPI, vol. 16(17), pages 1-21, August.
    11. Zhao, Zhen-Yu & Chang, Rui-Dong & Zillante, George, 2014. "Challenges for China׳s energy conservation and emission reduction," Energy Policy, Elsevier, vol. 74(C), pages 709-713.
    12. Liu, Aaron & Miller, Wendy & Cholette, Michael E. & Ledwich, Gerard & Crompton, Glenn & Li, Yong, 2021. "A multi-dimension clustering-based method for renewable energy investment planning," Renewable Energy, Elsevier, vol. 172(C), pages 651-666.
    13. Love Kumar & Farah Nadeem & Maggie Sloan & Jonas Restle-Steinert & Matthew J. Deitch & Sohail Ali Naqvi & Avinash Kumar & Claudio Sassanelli, 2022. "Fostering Green Finance for Sustainable Development: A Focus on Textile and Leather Small Medium Enterprises in Pakistan," Sustainability, MDPI, vol. 14(19), pages 1-24, September.
    14. Kusnandar & Indra Permana & Weiming Chiang & Fujen Wang & Changyu Liou, 2022. "Energy Consumption Analysis for Coupling Air Conditioners and Cold Storage Showcase Equipment in a Convenience Store," Energies, MDPI, vol. 15(13), pages 1-13, July.
    15. Zhan, Sicheng & Liu, Zhaoru & Chong, Adrian & Yan, Da, 2020. "Building categorization revisited: A clustering-based approach to using smart meter data for building energy benchmarking," Applied Energy, Elsevier, vol. 269(C).
    16. Debnath, Ramit & Bardhan, Ronita & Misra, Ashwin & Hong, Tianzhen & Rozite, Vida & Ramage, Michael H., 2022. "Lockdown impacts on residential electricity demand in India: A data-driven and non-intrusive load monitoring study using Gaussian mixture models," Energy Policy, Elsevier, vol. 164(C).
    17. Lo, Kevin, 2014. "A critical review of China's rapidly developing renewable energy and energy efficiency policies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 29(C), pages 508-516.
    18. Farah Shoukry & Rana Raafat & Khaled Tarabieh & Sherif Goubran, 2024. "Indoor Air Quality and Ventilation Energy in University Classrooms: Simplified Model to Predict Trade-Offs and Synergies," Sustainability, MDPI, vol. 16(7), pages 1-27, March.
    19. Li, Kehua & Yang, Rebecca Jing & Robinson, Duane & Ma, Jun & Ma, Zhenjun, 2019. "An agglomerative hierarchical clustering-based strategy using Shared Nearest Neighbours and multiple dissimilarity measures to identify typical daily electricity usage profiles of university library b," Energy, Elsevier, vol. 174(C), pages 735-748.
    20. Arash Khalilnejad & Ahmad M Karimi & Shreyas Kamath & Rojiar Haddadian & Roger H French & Alexis R Abramson, 2020. "Automated pipeline framework for processing of large-scale building energy time series data," PLOS ONE, Public Library of Science, vol. 15(12), pages 1-22, December.

    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:16:y:2024:i:22:p:9770-:d:1517169. 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.