Analyzing Long-Term and High Instantaneous Power Consumption of Buildings from Smart Meter Big Data with Deep Learning and Knowledge Graph Techniques
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-09-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/16/19/6893 |
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| author | 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 |
| author_facet | 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 |
| author_sort | Ru-Guan Wang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T21:45:38Z |
| format | Article |
| id | doaj.art-551ab732d77a4cd29b676e30de005d32 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T21:45:38Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-551ab732d77a4cd29b676e30de005d322023-11-19T14:20:20ZengMDPI AGEnergies1996-10732023-09-011619689310.3390/en16196893Analyzing Long-Term and High Instantaneous Power Consumption of Buildings from Smart Meter Big Data with Deep Learning and Knowledge Graph TechniquesRu-Guan Wang0Wen-Jen Ho1Kuei-Chun Chiang2Yung-Chieh Hung3Jen-Kuo Tai4Jia-Cheng Tan5Mei-Ling Chuang6Chi-Yun Ke7Yi-Fan Chien8An-Ping Jeng9Chien-Cheng Chou10Information Technology for Disaster Prevention (IT) Program, Department of Civil Engineering, National Central University, Taoyuan 32001, TaiwanDigital Transformation, Institute for Information Industry, Taipei 10574, TaiwanDigital Transformation, Institute for Information Industry, Taipei 10574, TaiwanDigital Transformation, Institute for Information Industry, Taipei 10574, TaiwanInformation Technology for Disaster Prevention (IT) Program, Department of Civil Engineering, National Central University, Taoyuan 32001, TaiwanInformation Technology for Disaster Prevention (IT) Program, Department of Civil Engineering, National Central University, Taoyuan 32001, TaiwanInformation Technology for Disaster Prevention (IT) Program, Department of Civil Engineering, National Central University, Taoyuan 32001, TaiwanInformation Technology for Disaster Prevention (IT) Program, Department of Civil Engineering, National Central University, Taoyuan 32001, TaiwanInformation Technology for Disaster Prevention (IT) Program, Department of Civil Engineering, National Central University, Taoyuan 32001, TaiwanInformation Technology for Disaster Prevention (IT) Program, Department of Civil Engineering, National Central University, Taoyuan 32001, TaiwanInformation Technology for Disaster Prevention (IT) Program, Department of Civil Engineering, National Central University, Taoyuan 32001, TaiwanIn 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.https://www.mdpi.com/1996-1073/16/19/6893smart meter data analyticstemporal databasedeep learning |
| spellingShingle | 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 Analyzing Long-Term and High Instantaneous Power Consumption of Buildings from Smart Meter Big Data with Deep Learning and Knowledge Graph Techniques Energies smart meter data analytics temporal database deep learning |
| title | Analyzing Long-Term and High Instantaneous Power Consumption of Buildings from Smart Meter Big Data with Deep Learning and Knowledge Graph Techniques |
| title_full | Analyzing Long-Term and High Instantaneous Power Consumption of Buildings from Smart Meter Big Data with Deep Learning and Knowledge Graph Techniques |
| title_fullStr | Analyzing Long-Term and High Instantaneous Power Consumption of Buildings from Smart Meter Big Data with Deep Learning and Knowledge Graph Techniques |
| title_full_unstemmed | Analyzing Long-Term and High Instantaneous Power Consumption of Buildings from Smart Meter Big Data with Deep Learning and Knowledge Graph Techniques |
| title_short | Analyzing Long-Term and High Instantaneous Power Consumption of Buildings from Smart Meter Big Data with Deep Learning and Knowledge Graph Techniques |
| title_sort | analyzing long term and high instantaneous power consumption of buildings from smart meter big data with deep learning and knowledge graph techniques |
| topic | smart meter data analytics temporal database deep learning |
| url | https://www.mdpi.com/1996-1073/16/19/6893 |
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