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Discovering gradual patterns in building operations for improving building energy efficiency

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  • Fan, Cheng
  • Sun, Yongjun
  • Shan, Kui
  • Xiao, Fu
  • Wang, Jiayuan

Abstract

The development of information technologies has enabled real-time monitoring and controls over building operations. Massive amounts of building operational data are being collected and available for knowledge discovery. Advanced data analytics are urgently needed to fully realize the potentials of big building operational data in enhancing building energy efficiency. The rapid development of data mining has provided powerful tools for extracting insights in various knowledge representations. Gradual pattern mining is a promising technique for discovering useful patterns from building operational data. The knowledge discovered is represented as gradual relationships, i.e., “the more/less A, the more/less B”. It can bring special interests to building energy management by highlighting co-variations among numerical building variables. This study investigated the usefulness of gradual pattern mining for building energy management. A generic methodology was proposed to ensure the quality and applicability of the knowledge discovered. The methodology was validated through a case study. The results showed that the methodology could successfully extract valuable insights on building operation characteristics and provide opportunities for building energy efficiency enhancement.

Suggested Citation

  • Fan, Cheng & Sun, Yongjun & Shan, Kui & Xiao, Fu & Wang, Jiayuan, 2018. "Discovering gradual patterns in building operations for improving building energy efficiency," Applied Energy, Elsevier, vol. 224(C), pages 116-123.
    • Handle: RePEc:eee:appene:v:224:y:2018:i:c:p:116-123
    DOI: 10.1016/j.apenergy.2018.04.118
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    1. Hong, Tianzhen & Piette, Mary Ann & Chen, Yixing & Lee, Sang Hoon & Taylor-Lange, Sarah C. & Zhang, Rongpeng & Sun, Kaiyu & Price, Phillip, 2015. "Commercial Building Energy Saver: An energy retrofit analysis toolkit," Applied Energy, Elsevier, vol. 159(C), pages 298-309.
    2. Wang, Huilong & Xu, Peng & Lu, Xing & Yuan, Dengkuo, 2016. "Methodology of comprehensive building energy performance diagnosis for large commercial buildings at multiple levels," Applied Energy, Elsevier, vol. 169(C), pages 14-27.
    3. Shan, Kui & Wang, Shengwei, 2017. "Energy efficient design and control of cleanroom environment control systems in subtropical regions – A comparative analysis and on-site validation," Applied Energy, Elsevier, vol. 204(C), pages 582-595.
    4. Xue, Puning & Zhou, Zhigang & Fang, Xiumu & Chen, Xin & Liu, Lin & Liu, Yaowen & Liu, Jing, 2017. "Fault detection and operation optimization in district heating substations based on data mining techniques," Applied Energy, Elsevier, vol. 205(C), pages 926-940.
    5. Monfet, Danielle & Zmeureanu, Radu, 2012. "Ongoing commissioning of water-cooled electric chillers using benchmarking models," Applied Energy, Elsevier, vol. 92(C), pages 99-108.
    6. Molina-Solana, Miguel & Ros, María & Ruiz, M. Dolores & Gómez-Romero, Juan & Martin-Bautista, M.J., 2017. "Data science for building energy management: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 598-609.
    7. Ma, Zhenjun & Wang, Shengwei, 2009. "Building energy research in Hong Kong: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(8), pages 1870-1883, October.
    8. Zhang, Rongpeng & Hong, Tianzhen, 2017. "Modeling of HVAC operational faults in building performance simulation," Applied Energy, Elsevier, vol. 202(C), pages 178-188.
    9. Raatikainen, Mika & Skön, Jukka-Pekka & Leiviskä, Kauko & Kolehmainen, Mikko, 2016. "Intelligent analysis of energy consumption in school buildings," Applied Energy, Elsevier, vol. 165(C), pages 416-429.
    10. Miller, Clayton & Nagy, Zoltán & Schlueter, Arno, 2018. "A review of unsupervised statistical learning and visual analytics techniques applied to performance analysis of non-residential buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1365-1377.
    11. Wang, Liping & Greenberg, Steve & Fiegel, John & Rubalcava, Alma & Earni, Shankar & Pang, Xiufeng & Yin, Rongxin & Woodworth, Spencer & Hernandez-Maldonado, Jorge, 2013. "Monitoring-based HVAC commissioning of an existing office building for energy efficiency," Applied Energy, Elsevier, vol. 102(C), pages 1382-1390.
    12. Li, Guannan & Hu, Yunpeng & Chen, Huanxin & Li, Haorong & Hu, Min & Guo, Yabin & Liu, Jiangyan & Sun, Shaobo & Sun, Miao, 2017. "Data partitioning and association mining for identifying VRF energy consumption patterns under various part loads and refrigerant charge conditions," Applied Energy, Elsevier, vol. 185(P1), pages 846-861.
    13. Du, Zhimin & Jin, Xinqiao & Fang, Xing & Fan, Bo, 2016. "A dual-benchmark based energy analysis method to evaluate control strategies for building HVAC systems," Applied Energy, Elsevier, vol. 183(C), pages 700-714.
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