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Analyzing Long-Term and High Instantaneous Power Consumption of Buildings from Smart Meter Big Data with Deep Learning and Knowledge Graph Techniques

Author

Listed:
  • Ru-Guan Wang

    (Information Technology for Disaster Prevention (IT) Program, Department of Civil Engineering, National Central University, Taoyuan 32001, Taiwan)

  • Wen-Jen Ho

    (Digital Transformation, Institute for Information Industry, Taipei 10574, Taiwan)

  • Kuei-Chun Chiang

    (Digital Transformation, Institute for Information Industry, Taipei 10574, Taiwan)

  • Yung-Chieh Hung

    (Digital Transformation, Institute for Information Industry, Taipei 10574, Taiwan)

  • Jen-Kuo Tai

    (Information Technology for Disaster Prevention (IT) Program, Department of Civil Engineering, National Central University, Taoyuan 32001, Taiwan)

  • Jia-Cheng Tan

    (Information Technology for Disaster Prevention (IT) Program, Department of Civil Engineering, National Central University, Taoyuan 32001, Taiwan)

  • Mei-Ling Chuang

    (Information Technology for Disaster Prevention (IT) Program, Department of Civil Engineering, National Central University, Taoyuan 32001, Taiwan)

  • Chi-Yun Ke

    (Information Technology for Disaster Prevention (IT) Program, Department of Civil Engineering, National Central University, Taoyuan 32001, Taiwan)

  • Yi-Fan Chien

    (Information Technology for Disaster Prevention (IT) Program, Department of Civil Engineering, National Central University, Taoyuan 32001, Taiwan
    Taoyuan Fire Department, Taoyuan 33054, Taiwan)

  • An-Ping Jeng

    (Information Technology for Disaster Prevention (IT) Program, Department of Civil Engineering, National Central University, Taoyuan 32001, Taiwan
    Taoyuan Fire Department, Taoyuan 33054, Taiwan)

  • Chien-Cheng Chou

    (Information Technology for Disaster Prevention (IT) Program, Department of Civil Engineering, National Central University, Taoyuan 32001, Taiwan)

Abstract

In the context of the growing emphasis on energy conservation and carbon reduction, the widespread deployment of smart meters in residential and commercial buildings is instrumental in promoting electricity savings. In Taiwan, local governments are actively promoting the installation of smart meters, empowering residents to monitor their electricity consumption and detect abnormal usage patterns, thus mitigating the risk of electrical fires. This safety-oriented approach is a significant driver behind the adoption of smart meters. However, the analysis of the substantial data generated by these meters necessitates pre-processing to address anomalies. Presently, these data primarily serve billing calculations or the extraction of power-saving patterns through big data analytics. To address these challenges, this study proposes a comprehensive approach that integrates a relational database for storing electricity consumption data with knowledge graphs. This integrated method effectively addresses data scarcity at various time scales and identifies prolonged periods of excessive electricity consumption, enabling timely alerts to residents for specific appliance shutdowns. Deep learning techniques are employed to analyze historical consumption data and real-time smart meter readings, with the goal of identifying and mitigating hazardous usage behavior, consequently reducing the risk of electrical fires. The research includes numerical values and text-based predictions for a comprehensive evaluation, utilizing data from ten Taiwanese households in 2022. The anticipated outcome is an improvement in household electrical safety and enhanced energy efficiency.

Suggested Citation

  • Ru-Guan Wang & Wen-Jen Ho & Kuei-Chun Chiang & Yung-Chieh Hung & Jen-Kuo Tai & Jia-Cheng Tan & Mei-Ling Chuang & Chi-Yun Ke & Yi-Fan Chien & An-Ping Jeng & Chien-Cheng Chou, 2023. "Analyzing Long-Term and High Instantaneous Power Consumption of Buildings from Smart Meter Big Data with Deep Learning and Knowledge Graph Techniques," Energies, MDPI, vol. 16(19), pages 1-24, September.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:19:p:6893-:d:1251236
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    References listed on IDEAS

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