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

Author

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  • 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
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    References listed on IDEAS

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    1. 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.
    2. 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.
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