IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i4p3118-d1062178.html
   My bibliography  Save this article

Energy Consumption and Carbon Dioxide Production Optimization in an Educational Building Using the Supported Vector Machine and Ant Colony System

Author

Listed:
  • Wongchai Anupong

    (Department of Agricultural Economy and Development, Faculty of Agriculture, Chiang Mai University, Chiang Mai 52000, Thailand)

  • Iskandar Muda

    (Department of Doctoral Program, Faculty Economic and Business, Universitas Sumatera Utara, Medan 20222, Indonesia)

  • Sabah Auda AbdulAmeer

    (Department of Mechanical Engineering, Ahl Al Bayt University, Kerbala 56001, Iraq)

  • Ibrahim H. Al-Kharsan

    (Computer Technical Engineering Department, College of Technical Engineering, The Islamic University, Najaf 54001, Iraq)

  • Aníbal Alviz-Meza

    (Grupo de Investigación en Deterioro de Materiales, Transición Energética y Ciencia de Datos DANT3, Facultad de Ingenieria y Urbanismo, Universidad Señor de Sipán, Km 5 Via Pimentel, Chiclayo 14001, Peru)

  • Yulineth Cárdenas-Escrocia

    (GIOPEN, Energy Optimization Research Group, Energy Department, Universidad de la Costa (CUC), Cl. 58 ##55–66, Barranquilla 080016, Atlántico, Colombia)

Abstract

Buildings account for sixty percent of the world’s total annual energy consumption; therefore, it is essential to find ways to reduce the amount of energy used in this sector. The road administration organization in Jakarta, Indonesia, utilized a questionnaire as well as the insights of industry experts to determine the most effective energy optimization parameters. It was decided to select variables such as the wall and ceiling materials, the number and type of windows, and the wall and ceiling insulation thickness. Several different modes were evaluated using the DesignBuilder software. Training the data with a supported vector machine (SVM) revealed the relationship between the inputs and the two critical outputs, namely the amount of energy consumption and CO 2 production, and the ant colony algorithm was used for optimization. According to the findings, the ratio of the north and east windows to the wall in one direction is 70 percent, while the ratio of the south window to the wall in the same direction ranges from 35 to 50 percent. When the ratio and percentage of the west window to the west wall is between 60 and 70 percent, the amount of produced energy and CO 2 is reduced to negligible levels.

Suggested Citation

  • Wongchai Anupong & Iskandar Muda & Sabah Auda AbdulAmeer & Ibrahim H. Al-Kharsan & Aníbal Alviz-Meza & Yulineth Cárdenas-Escrocia, 2023. "Energy Consumption and Carbon Dioxide Production Optimization in an Educational Building Using the Supported Vector Machine and Ant Colony System," Sustainability, MDPI, vol. 15(4), pages 1-14, February.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:4:p:3118-:d:1062178
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/4/3118/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/15/4/3118/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Reza Alayi & Mahdi Mohkam & Seyed Reza Seyednouri & Mohammad Hossein Ahmadi & Mohsen Sharifpur, 2021. "Energy/Economic Analysis and Optimization of On-Grid Photovoltaic System Using CPSO Algorithm," Sustainability, MDPI, vol. 13(22), pages 1-16, November.
    2. Fatemeh Yavari & Seyyed Ali Salehi Neyshabouri & Jafar Yazdi & Amir Molajou & Adam Brysiewicz, 2022. "A Novel Framework for Urban Flood damage Assessment," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(6), pages 1991-2011, April.
    3. Oluyomi A. Osobajo & Afolabi Otitoju & Martha Ajibola Otitoju & Adekunle Oke, 2020. "The Impact of Energy Consumption and Economic Growth on Carbon Dioxide Emissions," Sustainability, MDPI, vol. 12(19), pages 1-16, September.
    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. Ren, Siyu & Hao, Yu & Xu, Lu & Wu, Haitao & Ba, Ning, 2021. "Digitalization and energy: How does internet development affect China's energy consumption?," Energy Economics, Elsevier, vol. 98(C).
    6. Gajpal, Yuvraj & Abad, P.L., 2009. "Multi-ant colony system (MACS) for a vehicle routing problem with backhauls," European Journal of Operational Research, Elsevier, vol. 196(1), pages 102-117, July.
    7. Johannes Sedlmeir & Hans Ulrich Buhl & Gilbert Fridgen & Robert Keller, 2020. "The Energy Consumption of Blockchain Technology: Beyond Myth," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 62(6), pages 599-608, December.
    8. Amir Molajou & Parsa Pouladi & Abbas Afshar, 2021. "Incorporating Social System into Water-Food-Energy Nexus," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(13), pages 4561-4580, October.
    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. Hisham Alghamdi & Aníbal Alviz-Meza, 2023. "A Novel Strategy for Converting Conventional Structures into Net-Zero-Energy Buildings without Destruction," Sustainability, MDPI, vol. 15(14), pages 1-14, July.
    2. Hisham Alghamdi & Aníbal Alviz-Meza, 2023. "Techno-Environmental Evaluation and Optimization of a Hybrid System: Application of Numerical Simulation and Gray Wolf Algorithm in Saudi Arabia," Sustainability, MDPI, vol. 15(18), pages 1-17, September.

    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. Zhao, Fei & Wang, Yuliang & Guo, Jianlong & Wu, Lifeng, 2024. "Chinese provincial energy consumption intensity prediction by the CGM(1,1)," Energy, Elsevier, vol. 292(C).
    2. Junhong Qu & Xiaoli Hao, 2022. "Digital Economy, Financial Development, and Energy Poverty Based on Mediating Effects and a Spatial Autocorrelation Model," Sustainability, MDPI, vol. 14(15), pages 1-24, July.
    3. Radoslaw Miskiewicz, 2022. "Clean and Affordable Energy within Sustainable Development Goals: The Role of Governance Digitalization," Energies, MDPI, vol. 15(24), pages 1-17, December.
    4. Zhenxiang Cao & Liqing Peng, 2023. "The Impact of Digital Economics on Environmental Quality: A System Dynamics Approach," SAGE Open, , vol. 13(4), pages 21582440231, December.
    5. Zhenkai Yang & Mei-Chih Wang & Tsangyao Chang & Wing-Keung Wong & Fangjhy Li, 2022. "Which Factors Determine CO 2 Emissions in China? Trade Openness, Financial Development, Coal Consumption, Economic Growth or Urbanization: Quantile Granger Causality Test," Energies, MDPI, vol. 15(7), pages 1-18, March.
    6. Hayat Khan & Liu Weili & Itbar Khan, 2022. "Environmental innovation, trade openness and quality institutions: an integrated investigation about environmental sustainability," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(3), pages 3832-3862, March.
    7. Zhipeng Yu & Yi Liu & Taihua Yan & Ming Zhang, 2024. "Carbon emission efficiency in the age of digital economy: New insights on green technology progress and industrial structure distortion," Business Strategy and the Environment, Wiley Blackwell, vol. 33(5), pages 4039-4057, July.
    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. Hong, Junjie & Shi, Fangyuan & Zheng, Yuhan, 2023. "Does network infrastructure construction reduce energy intensity? Based on the “Broadband China” strategy," Technological Forecasting and Social Change, Elsevier, vol. 190(C).
    10. Vincent Carrières & Andrée-Anne Lemieux & Manuele Margni & Robert Pellerin & Sylvain Cariou, 2022. "Measuring the Value of Blockchain Traceability in Supporting LCA for Textile Products," Sustainability, MDPI, vol. 14(4), pages 1-15, February.
    11. 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.
    12. Rongwu Zhang & Wenqiang Fu & Yingxu Kuang, 2022. "Can Digital Economy Promote Energy Conservation and Emission Reduction in Heavily Polluting Enterprises? Empirical Evidence from China," IJERPH, MDPI, vol. 19(16), pages 1-21, August.
    13. Daniela Nicoleta Sahlian & Adriana Florina Popa & Raluca Florentina Creţu, 2021. "Does the Increase in Renewable Energy Influence GDP Growth? An EU-28 Analysis," Energies, MDPI, vol. 14(16), pages 1-16, August.
    14. Yurika Pant Khanal & Abeer Alsadoon & Khurram Shahzad & Ahmad B. Al-Khalil & Penatiyana W. C. Prasad & Sabih Ur Rehman & Rafiqul Islam, 2022. "Utilizing Blockchain for IoT Privacy through Enhanced ECIES with Secure Hash Function," Future Internet, MDPI, vol. 14(3), pages 1-17, February.
    15. Zhou, Zhongsheng & Li, Zhuo, 2023. "Corporate digital transformation and trade credit financing," Journal of Business Research, Elsevier, vol. 160(C).
    16. Mengyao Liu & Yan Hou & Hongli Jiang, 2023. "The Energy-Saving Effect of E-Commerce Development—A Quasi-Natural Experiment in China," Energies, MDPI, vol. 16(12), pages 1-22, June.
    17. Zihanxin Li & Nuoyan Li & Huwei Wen, 2021. "Digital Economy and Environmental Quality: Evidence from 217 Cities in China," Sustainability, MDPI, vol. 13(14), pages 1-20, July.
    18. Wei Yu & Huiqin Huang & Xinyan Kong & Keying Zhu, 2023. "Can Digital Inclusive Finance Improve the Financial Performance of SMEs?," Sustainability, MDPI, vol. 15(3), pages 1-16, January.
    19. Ding, Qian & Huang, Jianbai & Chen, Jinyu & Tao, Dali, 2023. "Internet development and renewable energy technological innovation: Does institutional quality matter?," Renewable Energy, Elsevier, vol. 218(C).
    20. Bo Li & Jing Liu & Qian Liu & Muhammad Mohiuddin, 2022. "The Effects of Broadband Infrastructure on Carbon Emission Efficiency of Resource-Based Cities in China: A Quasi-Natural Experiment from the “Broadband China” Pilot Policy," IJERPH, MDPI, vol. 19(11), pages 1-27, May.

    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:15:y:2023:i:4:p:3118-:d:1062178. 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.