IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i24p16619-d1295272.html
   My bibliography  Save this article

An Adaptive Control Model for Thermal Environmental Factors to Supplement the Sustainability of a Small-Sized Factory

Author

Listed:
  • Jonghoon Ahn

    (Major in Architectural Engineering, School of Architecture and Design Convergence, Hankyong National University, Anseong-si 17579, Republic of Korea)

Abstract

Effective indoor thermal controls can have quantifiable advantages of improving energy efficiency and indoor environmental quality, which can also lead to additional benefits such as better workability, productivity, and economy in buildings. However, in the case of factory buildings whose main usage is to produce and process goods, securing thermal comfort for their workers has been regarded as a secondary problem. This study aims to explore the method for cooling and heating air supply controls to improve the thermal comfort of factory buildings by use of a data-driven adaptive model. The genetic algorithm using the idea of occupancy rate helps the model to effectively analyze the indoor environment to determine the optimized conditions for energy use and thermal comfort. As a result, the proposed model successfully shows better performance, which confirms that there is a 2.81% saving in energy consumption and a 16–32% reduction in indoor thermal dissatisfaction. In particular, the significance of this study is that energy use and thermal dissatisfaction can be reduced simultaneously despite precise air-supply controls that are performed in response to the conditions of the building, weather, and occupancy rate.

Suggested Citation

  • Jonghoon Ahn, 2023. "An Adaptive Control Model for Thermal Environmental Factors to Supplement the Sustainability of a Small-Sized Factory," Sustainability, MDPI, vol. 15(24), pages 1-15, December.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:24:p:16619-:d:1295272
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/24/16619/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/24/16619/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Domenico Palladino & Iole Nardi & Cinzia Buratti, 2020. "Artificial Neural Network for the Thermal Comfort Index Prediction: Development of a New Simplified Algorithm," Energies, MDPI, vol. 13(17), pages 1-27, September.
    2. Cihan Turhan & Ali Serdar Atalay & Gulden Gokcen Akkurt, 2023. "An Integrated Decision-Making Framework for Mitigating the Impact of Urban Heat Islands on Energy Consumption and Thermal Comfort of Residential Buildings," Sustainability, MDPI, vol. 15(12), pages 1-23, June.
    3. Xinge Du & Guoyao Gao & Feng Gao & Zhihua Zhou, 2023. "A Study on Modifying Campus Buildings to Improve Habitat Comfort—A Case Study of Tianjin University Campus," Sustainability, MDPI, vol. 15(19), pages 1-24, September.
    4. DeQuante Rashon Mckoy & Raymond Charles Tesiero & Yaa Takyiwaa Acquaah & Balakrishna Gokaraju, 2023. "Review of HVAC Systems History and Future Applications," Energies, MDPI, vol. 16(17), pages 1-15, August.
    5. Aleksander Skała & Jakub Grela & Dominik Latoń & Katarzyna Bańczyk & Michał Markiewicz & Andrzej Ożadowicz, 2023. "Implementation of Building a Thermal Model to Improve Energy Efficiency of the Central Heating System—A Case Study," Energies, MDPI, vol. 16(19), pages 1-27, September.
    6. Bojana Lević & Ljiljana Đukanović & Dušan Ignjatović, 2023. "Approaches for Complex and Integrated Refurbishment to Improve Energy Efficiency and Spatial Comfort of the Existing Post-War Mass Housing Stock in Serbia," Sustainability, MDPI, vol. 15(18), pages 1-31, September.
    7. Adolfo Crespo Márquez & Antonio de la Fuente Carmona & Sara Antomarioni, 2019. "A Process to Implement an Artificial Neural Network and Association Rules Techniques to Improve Asset Performance and Energy Efficiency," Energies, MDPI, vol. 12(18), pages 1-25, September.
    8. Ahn, Jonghoon & Cho, Soolyeon, 2017. "Anti-logic or common sense that can hinder machine’s energy performance: Energy and comfort control models based on artificial intelligence responding to abnormal indoor environments," Applied Energy, Elsevier, vol. 204(C), pages 117-130.
    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. Jonghoon Ahn, 2022. "A Network-Based Strategy to Increase the Sustainability of Building Supply Air Systems Responding to Unexpected Temperature Patterns," Sustainability, MDPI, vol. 14(22), pages 1-13, November.
    2. Lee-Yong Sung & Jonghoon Ahn, 2020. "Comparative Analyses of Energy Efficiency between on-Demand and Predictive Controls for Buildings’ Indoor Thermal Environment," Energies, MDPI, vol. 13(5), pages 1-15, March.
    3. Majda Ćesić & Katarina Rogulj & Jelena Kilić Pamuković & Andrija Krtalić, 2024. "A Systematic Review on Fuzzy Decision Support Systems and Multi-Criteria Analysis in Urban Heat Island Management," Energies, MDPI, vol. 17(9), pages 1-41, April.
    4. Hugo O. Garcés & Claudia Durán & Eduardo Espinosa & Alejandro Jerez & Fredi Palominos & Marcela Hinojosa & Raúl Carrasco, 2022. "Monitoring of Thermal Comfort and Air Quality for Sustainable Energy Management inside Hospitals Based on Online Analytical Processing and the Internet of Things," IJERPH, MDPI, vol. 19(19), pages 1-23, September.
    5. Davide Coraci & Silvio Brandi & Marco Savino Piscitelli & Alfonso Capozzoli, 2021. "Online Implementation of a Soft Actor-Critic Agent to Enhance Indoor Temperature Control and Energy Efficiency in Buildings," Energies, MDPI, vol. 14(4), pages 1-26, February.
    6. Rediske, Graciele & Michels, Leandro & Siluk, Julio Cezar Mairesse & Rigo, Paula Donaduzzi & Rosa, Carmen Brum & Lima, Andrei Cunha, 2024. "A proposed set of indicators for evaluating the performance of the operation and maintenance of photovoltaic plants," Applied Energy, Elsevier, vol. 354(PA).
    7. Yonatal Tefera & Veronica Soebarto & Courtney Bishop & John Kandulu & Carmel Williams, 2023. "A Scoping Review of Urban Planning Decision Support Tools and Processes That Account for the Health, Environment, and Economic Benefits of Trees and Greenspace," IJERPH, MDPI, vol. 21(1), pages 1-28, December.
    8. James Ogundiran & Ehsan Asadi & Manuel Gameiro da Silva, 2024. "A Systematic Review on the Use of AI for Energy Efficiency and Indoor Environmental Quality in Buildings," Sustainability, MDPI, vol. 16(9), pages 1-30, April.
    9. Jonghoon Ahn, 2020. "Improvement of the Performance Balance between Thermal Comfort and Energy Use for a Building Space in the Mid-Spring Season," Sustainability, MDPI, vol. 12(22), pages 1-14, November.
    10. Piotr Michalak, 2021. "Selected Aspects of Indoor Climate in a Passive Office Building with a Thermally Activated Building System: A Case Study from Poland," Energies, MDPI, vol. 14(4), pages 1-22, February.
    11. Ioannis Mallidis & Volha Yakavenka & Anastasios Konstantinidis & Nikolaos Sariannidis, 2021. "A Goal Programming-Based Methodology for Machine Learning Model Selection Decisions: A Predictive Maintenance Application," Mathematics, MDPI, vol. 9(19), pages 1-16, September.
    12. Zhao, Haitao & Ezeh, Collins I. & Ren, Weijia & Li, Wentao & Pang, Cheng Heng & Zheng, Chenghang & Gao, Xiang & Wu, Tao, 2019. "Integration of machine learning approaches for accelerated discovery of transition-metal dichalcogenides as Hg0 sensing materials," Applied Energy, Elsevier, vol. 254(C).
    13. 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.
    14. Zheng, Xiaolei & Nguyen, Hoang & Bui, Xuan-Nam, 2021. "Exploring the relation between production factors, ore grades, and life of mine for forecasting mining capital cost through a novel cascade forward neural network-based salp swarm optimization model," Resources Policy, Elsevier, vol. 74(C).
    15. 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).
    16. Amal Azzi & Mohamed Tabaa & Badr Chegari & Hanaa Hachimi, 2024. "Balancing Sustainability and Comfort: A Holistic Study of Building Control Strategies That Meet the Global Standards for Efficiency and Thermal Comfort," Sustainability, MDPI, vol. 16(5), pages 1-36, March.
    17. Jonghoon Ahn, 2020. "Performance Analyses of Temperature Controls by a Network-Based Learning Controller for an Indoor Space in a Cold Area," Sustainability, MDPI, vol. 12(20), pages 1-17, October.
    18. Aldemar Leguizamon-Perilla & Juan S. Rodriguez-Bernal & Laidi Moralez-Cruz & Nidia Isabel Farfán-Martinez & César Nieto-Londoño & Rafael E. Vásquez & Ana Escudero-Atehortua, 2023. "Digitalisation and Modernisation of Hydropower Operating Facilities to Support the Colombian Energy Mix Flexibility," Energies, MDPI, vol. 16(7), pages 1-17, March.
    19. Sara Antomarioni & Marjorie Maria Bellinello & Maurizio Bevilacqua & Filippo Emanuele Ciarapica & Renan Favarão da Silva & Gilberto Francisco Martha de Souza, 2020. "A Data-Driven Approach to Extend Failure Analysis: A Framework Development and a Case Study on a Hydroelectric Power Plant," Energies, MDPI, vol. 13(23), pages 1-16, December.
    20. Adolfo Crespo Marquez & Juan Francisco Gomez Fernandez & Pablo Martínez-Galán Fernández & Antonio Guillen Lopez, 2020. "Maintenance Management through Intelligent Asset Management Platforms (IAMP). Emerging Factors, Key Impact Areas and Data Models," Energies, MDPI, vol. 13(15), pages 1-19, July.

    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:24:p:16619-:d:1295272. 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.