IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i18p13671-d1238883.html
   My bibliography  Save this article

Enhancing Zero-Carbon Building Operation and Maintenance: A Correlation-Based Data Mining Approach for Database Analysis

Author

Listed:
  • Yuhong Zhao

    (Faculty of Architecture, Civil and Transportation Engineering, Beijing University of Technology, Beijing 100124, China
    Key Laboratory of Urban Security and Disaster Engineering of Ministry of Education, Beijing University of Technology, Beijing 100124, China)

  • Ruirui Liu

    (Faculty of Architecture, Civil and Transportation Engineering, Beijing University of Technology, Beijing 100124, China
    Key Laboratory of Urban Security and Disaster Engineering of Ministry of Education, Beijing University of Technology, Beijing 100124, China)

  • Zhansheng Liu

    (Faculty of Architecture, Civil and Transportation Engineering, Beijing University of Technology, Beijing 100124, China
    Key Laboratory of Urban Security and Disaster Engineering of Ministry of Education, Beijing University of Technology, Beijing 100124, China)

  • Yun Lu

    (Shangxinzhuang Canal Management Office in Huangzhong District, Xining 811600, China)

  • Liang Liu

    (Faculty of Architecture, Civil and Transportation Engineering, Beijing University of Technology, Beijing 100124, China
    Key Laboratory of Urban Security and Disaster Engineering of Ministry of Education, Beijing University of Technology, Beijing 100124, China)

  • Jingjing Wang

    (Faculty of Architecture, Civil and Transportation Engineering, Beijing University of Technology, Beijing 100124, China
    Key Laboratory of Urban Security and Disaster Engineering of Ministry of Education, Beijing University of Technology, Beijing 100124, China)

  • Wenxiang Liu

    (Faculty of Architecture, Civil and Transportation Engineering, Beijing University of Technology, Beijing 100124, China
    Key Laboratory of Urban Security and Disaster Engineering of Ministry of Education, Beijing University of Technology, Beijing 100124, China)

Abstract

In the context of global climate change and the increasing focus on carbon emissions, carbon emission research has become a prominent area of study. However, research in this field inevitably involves extensive monitoring, and when the data become complex and chaotic, the accuracy of these data can be challenging to control, making it difficult to determine their reliability. This article starts by exploring the operational and maintenance data of zero-carbon buildings, aiming to uncover the correlation between energy consumption data and environmental data. This correlation is categorized into two main types: linear correlation and trend correlation. By establishing error degree calculations based on these correlation relationships, anomaly detection can be performed on the data. Analyzing the interrelationships between these datasets allows for the formulation of appropriate fitting equations, primarily consisting of linear and polynomial fits, all of which exhibit a determination coefficient exceeding 0.99. These fitting equations are then utilized to correct errors in the anomalous data, and the reasonableness of the fitting methods is demonstrated by examining the residual distribution. The final results align with the corresponding expectations, providing a concise and effective correction method for monitoring data in zero-carbon smart buildings. Importantly, this method exhibits a certain level of generality and can be applied to various scenarios within the realm of zero-carbon buildings.

Suggested Citation

  • Yuhong Zhao & Ruirui Liu & Zhansheng Liu & Yun Lu & Liang Liu & Jingjing Wang & Wenxiang Liu, 2023. "Enhancing Zero-Carbon Building Operation and Maintenance: A Correlation-Based Data Mining Approach for Database Analysis," Sustainability, MDPI, vol. 15(18), pages 1-16, September.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:18:p:13671-:d:1238883
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/18/13671/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/18/13671/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Polina Trofimova & Ali Cheshmehzangi & Wu Deng & Craig Hancock, 2021. "Post-Occupancy Evaluation of Indoor Air Quality and Thermal Performance in a Zero Carbon Building," Sustainability, MDPI, vol. 13(2), pages 1-21, January.
    2. Nicole Anderson & Gayan Wedawatta & Ishara Rathnayake & Niluka Domingo & Zahirah Azizi, 2022. "Embodied Energy Consumption in the Residential Sector: A Case Study of Affordable Housing," Sustainability, MDPI, vol. 14(9), pages 1-18, April.
    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. Seyedmohammadreza Heibati & Wahid Maref & Hamed H. Saber, 2021. "Assessing the Energy, Indoor Air Quality, and Moisture Performance for a Three-Story Building Using an Integrated Model, Part Three: Development of Integrated Model and Applications," Energies, MDPI, vol. 14(18), pages 1-31, September.
    2. Dorota Chwieduk & Bartosz Chwieduk, 2023. "Application of Heat Pumps in New Housing Estates in Cities Suburbs as an Means of Energy Transformation in Poland," Energies, MDPI, vol. 16(8), pages 1-19, April.
    3. Su, Yuan & Wang, Linwei & Feng, Wei & Zhou, Nan & Wang, Luyuan, 2021. "Analysis of green building performance in cold coastal climates: An in-depth evaluation of green buildings in Dalian, China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 146(C).
    4. Ljubomir Jankovic & Silvio Carta, 2021. "BioZero—Designing Nature-Inspired Net-Zero Building," Sustainability, MDPI, vol. 13(14), pages 1-23, July.
    5. Michele Zinzi & Francesca Pagliaro & Stefano Agnoli & Fabio Bisegna & Domenico Iatauro, 2021. "On the Built-Environment Quality in Nearly Zero-Energy Renovated Schools: Assessment and Impact of Passive Strategies," Energies, MDPI, vol. 14(10), pages 1-18, 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:18:p:13671-:d:1238883. 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.