IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v253y2019ic94.html
   My bibliography  Save this article

An improved association rule mining-based method for revealing operational problems of building heating, ventilation and air conditioning (HVAC) systems

Author

Listed:
  • Zhang, Chaobo
  • Xue, Xue
  • Zhao, Yang
  • Zhang, Xuejun
  • Li, Tingting

Abstract

Energy wastes in heating, ventilation and air conditioning (HVAC) systems of buildings are very common due to lots of operational problems. It is in great need to develop data mining-based methods to discover these operational problems from the historical data of HVAC systems. In the past years, researchers had realized that association rule mining was one of the most effective algorithms to solve this problem. But, most of the mined operational patterns are useless. It is time-consuming to check them manually. In this study, an improved association rule mining-based method is proposed to enhance the performance of data mining and to filter out useless rules automatically. It contains three steps, i.e., data preprocessing, association rule mining and post mining. In the step of data preprocessing, a kernel density estimation-based approach is developed to filter out outliers automatically. And, a kernel density estimation-based approach is developed to transform numerical data into categorical data automatically. In the step of association rule mining, the FP-growth algorithm is utilized to extract raw association rules from the preprocessed data. In the step of post mining, a novel comparison-based approach is developed to reduce the amount of useless association rules. Evaluations are made using the historical operational data of the chiller plant of a commercial building. Results show that the proposed data preprocessing approaches are effective in outlier identification and data transformation. And, the proposed comparison-based approach can filter out 54.98% of the mined association rules automatically which are useless for discovering operational problems.

Suggested Citation

  • Zhang, Chaobo & Xue, Xue & Zhao, Yang & Zhang, Xuejun & Li, Tingting, 2019. "An improved association rule mining-based method for revealing operational problems of building heating, ventilation and air conditioning (HVAC) systems," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    • Handle: RePEc:eee:appene:v:253:y:2019:i:c:94
    DOI: 10.1016/j.apenergy.2019.113492
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261919311663
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2019.113492?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Astolfi, Davide & Castellani, Francesco & Garinei, Alberto & Terzi, Ludovico, 2015. "Data mining techniques for performance analysis of onshore wind farms," Applied Energy, Elsevier, vol. 148(C), pages 220-233.
    2. 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.
    3. Xydas, Erotokritos & Marmaras, Charalampos & Cipcigan, Liana M. & Jenkins, Nick & Carroll, Steve & Barker, Myles, 2016. "A data-driven approach for characterising the charging demand of electric vehicles: A UK case study," Applied Energy, Elsevier, vol. 162(C), pages 763-771.
    4. Fan, Cheng & Xiao, Fu & Zhao, Yang, 2017. "A short-term building cooling load prediction method using deep learning algorithms," Applied Energy, Elsevier, vol. 195(C), pages 222-233.
    5. Hong, Tianzhen & Yang, Le & Hill, David & Feng, Wei, 2014. "Data and analytics to inform energy retrofit of high performance buildings," Applied Energy, Elsevier, vol. 126(C), pages 90-106.
    6. Chen, Yujiao & Tong, Zheming & Wu, Wentao & Samuelson, Holly & Malkawi, Ali & Norford, Leslie, 2019. "Achieving natural ventilation potential in practice: Control schemes and levels of automation," Applied Energy, Elsevier, vol. 235(C), pages 1141-1152.
    7. 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.
    8. Pérez-Martín, A. & Pérez-Torregrosa, A. & Vaca, M., 2018. "Big Data techniques to measure credit banking risk in home equity loans," Journal of Business Research, Elsevier, vol. 89(C), pages 448-454.
    9. Chen, Yujiao & Malkawi, Ali & Liu, Zhu & Freeman, Richard Barry & Tong, Zheming, 2016. "Energy Saving Potential of Natural Ventilation in China: The Impact of Ambient Air Pollution," Scholarly Articles 27733689, Harvard University Department of Economics.
    10. Zhang, Zijun & Kusiak, Andrew & Zeng, Yaohui & Wei, Xiupeng, 2016. "Modeling and optimization of a wastewater pumping system with data-mining methods," Applied Energy, Elsevier, vol. 164(C), pages 303-311.
    11. 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.
    12. Tong, Zheming & Chen, Yujiao & Malkawi, Ali & Liu, Zhu & Freeman, Richard B., 2016. "Energy saving potential of natural ventilation in China: The impact of ambient air pollution," Applied Energy, Elsevier, vol. 179(C), pages 660-668.
    13. Zhao, Yang & Li, Tingting & Zhang, Xuejun & Zhang, Chaobo, 2019. "Artificial intelligence-based fault detection and diagnosis methods for building energy systems: Advantages, challenges and the future," Renewable and Sustainable Energy Reviews, Elsevier, vol. 109(C), pages 85-101.
    14. Hubert, M. & Vandervieren, E., 2008. "An adjusted boxplot for skewed distributions," Computational Statistics & Data Analysis, Elsevier, vol. 52(12), pages 5186-5201, August.
    15. Wang, Yichuan & Kung, LeeAnn & Byrd, Terry Anthony, 2018. "Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations," Technological Forecasting and Social Change, Elsevier, vol. 126(C), pages 3-13.
    16. Tong, Zheming & Chen, Yujiao & Malkawi, Ali, 2017. "Estimating natural ventilation potential for high-rise buildings considering boundary layer meteorology," Applied Energy, Elsevier, vol. 193(C), pages 276-286.
    17. Yu, Zhun (Jerry) & Haghighat, Fariborz & Fung, Benjamin C.M. & Morofsky, Edward & Yoshino, Hiroshi, 2011. "A methodology for identifying and improving occupant behavior in residential buildings," Energy, Elsevier, vol. 36(11), pages 6596-6608.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Mustapha, Rasha & Zoughaib, Assaad & Ghaddar, Nesreen & Ghali, Kamel, 2020. "Modified upright cup method for testing water vapor permeability in porous membranes," Energy, Elsevier, vol. 195(C).
    2. Sun, Chunhua & Yuan, Lingyu & Cao, Shanshan & Xia, Guoqiang & Liu, Yanan & Wu, Xiangdong, 2023. "Identifying supply-demand mismatches in district heating system based on association rule mining," Energy, Elsevier, vol. 280(C).
    3. Savadkoohi, Marjan & Macarulla, Marcel & Casals, Miquel, 2023. "Facilitating the implementation of neural network-based predictive control to optimize building heating operation," Energy, Elsevier, vol. 263(PB).
    4. Elsa Chaerun Nisa & Yean-Der Kuan & Chin-Chang Lai, 2021. "Chiller Optimization Using Data Mining Based on Prediction Model, Clustering and Association Rule Mining," Energies, MDPI, vol. 14(20), pages 1-14, October.
    5. Zhang, Chaobo & Li, Junyang & Zhao, Yang & Li, Tingting & Chen, Qi & Zhang, Xuejun & Qiu, Weikang, 2021. "Problem of data imbalance in building energy load prediction: Concept, influence, and solution," Applied Energy, Elsevier, vol. 297(C).
    6. Fan, Cheng & Chen, Ruikun & Mo, Jinhan & Liao, Longhui, 2024. "Personalized federated learning for cross-building energy knowledge sharing: Cost-effective strategies and model architectures," Applied Energy, Elsevier, vol. 362(C).
    7. Huang, Ke & Lu, Shilei & Han, Zhao & Yuan, Jianjuan, 2023. "Research on heat consumption detection, restoration and prediction methods for discontinuous heating substation," Energy, Elsevier, vol. 266(C).
    8. Xie, Kang & Hui, Hongxun & Ding, Yi & Song, Yonghua & Ye, Chengjin & Zheng, Wandong & Ye, Shuiquan, 2022. "Modeling and control of central air conditionings for providing regulation services for power systems," Applied Energy, Elsevier, vol. 315(C).
    9. Du, Han & Zhou, Xinlei & Nord, Natasa & Carden, Yale & Ma, Zhenjun, 2023. "A new data mining strategy for performance evaluation of a shared energy recovery system integrated with data centres and district heating networks," Energy, Elsevier, vol. 285(C).
    10. Du, Zhimin & Liang, Xinbin & Chen, Siliang & Li, Pengcheng & Zhu, Xu & Chen, Kang & Jin, Xinqiao, 2023. "Domain adaptation deep learning and its T-S diagnosis networks for the cross-control and cross-condition scenarios in data center HVAC systems," Energy, Elsevier, vol. 280(C).

    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. Zhao, Yang & Li, Tingting & Zhang, Xuejun & Zhang, Chaobo, 2019. "Artificial intelligence-based fault detection and diagnosis methods for building energy systems: Advantages, challenges and the future," Renewable and Sustainable Energy Reviews, Elsevier, vol. 109(C), pages 85-101.
    2. Rongjiang Ma & Shen Yang & Xianlin Wang & Xi-Cheng Wang & Ming Shan & Nanyang Yu & Xudong Yang, 2020. "Systematic Method for the Energy-Saving Potential Calculation of Air-Conditioning Systems via Data Mining. Part I: Methodology," Energies, MDPI, vol. 14(1), pages 1-15, December.
    3. Payam Nejat & Fatemeh Jomehzadeh & Hasanen Mohammed Hussen & John Kaiser Calautit & Muhd Zaimi Abd Majid, 2018. "Application of Wind as a Renewable Energy Source for Passive Cooling through Windcatchers Integrated with Wing Walls," Energies, MDPI, vol. 11(10), pages 1-23, September.
    4. Li, Tingting & Zhou, Yangze & Zhao, Yang & Zhang, Chaobo & Zhang, Xuejun, 2022. "A hierarchical object oriented Bayesian network-based fault diagnosis method for building energy systems," Applied Energy, Elsevier, vol. 306(PB).
    5. Hiroshi Mori & Tetsu Kubota & I Gusti Ngurah Antaryama & Sri Nastiti N. Ekasiwi, 2020. "Analysis of Window-Opening Patterns and Air Conditioning Usage of Urban Residences in Tropical Southeast Asia," Sustainability, MDPI, vol. 12(24), pages 1-21, December.
    6. Chen, Yujiao & Tong, Zheming & Wu, Wentao & Samuelson, Holly & Malkawi, Ali & Norford, Leslie, 2019. "Achieving natural ventilation potential in practice: Control schemes and levels of automation," Applied Energy, Elsevier, vol. 235(C), pages 1141-1152.
    7. Fan, Yuling & Xia, Xiaohua, 2018. "Building retrofit optimization models using notch test data considering energy performance certificate compliance," Applied Energy, Elsevier, vol. 228(C), pages 2140-2152.
    8. Bienvenido-Huertas, David & Sánchez-García, Daniel & Rubio-Bellido, Carlos, 2020. "Analysing natural ventilation to reduce the cooling energy consumption and the fuel poverty of social dwellings in coastal zones," Applied Energy, Elsevier, vol. 279(C).
    9. Sakiyama, N.R.M. & Carlo, J.C. & Frick, J. & Garrecht, H., 2020. "Perspectives of naturally ventilated buildings: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 130(C).
    10. Nutkiewicz, Alex & Jain, Rishee K. & Bardhan, Ronita, 2018. "Energy modeling of urban informal settlement redevelopment: Exploring design parameters for optimal thermal comfort in Dharavi, Mumbai, India," Applied Energy, Elsevier, vol. 231(C), pages 433-445.
    11. Tong, Shuiguang & Cheng, Zhewu & Cong, Feiyun & Tong, Zheming & Zhang, Yidong, 2018. "Developing a grid-connected power optimization strategy for the integration of wind power with low-temperature adiabatic compressed air energy storage," Renewable Energy, Elsevier, vol. 125(C), pages 73-86.
    12. Liu, Jiangyan & Zhang, Qing & Dong, Zhenxiang & Li, Xin & Li, Guannan & Xie, Yi & Li, Kuining, 2021. "Quantitative evaluation of the building energy performance based on short-term energy predictions," Energy, Elsevier, vol. 223(C).
    13. Peter Rafaj & Markus Amann, 2018. "Decomposing Air Pollutant Emissions in Asia: Determinants and Projections," Energies, MDPI, vol. 11(5), pages 1-14, May.
    14. Chen, Zhelun & O’Neill, Zheng & Wen, Jin & Pradhan, Ojas & Yang, Tao & Lu, Xing & Lin, Guanjing & Miyata, Shohei & Lee, Seungjae & Shen, Chou & Chiosa, Roberto & Piscitelli, Marco Savino & Capozzoli, , 2023. "A review of data-driven fault detection and diagnostics for building HVAC systems," Applied Energy, Elsevier, vol. 339(C).
    15. Zhang, Shaohui & Guo, Qinxin & Smyth, Russell & Yao, Yao, 2022. "Extreme temperatures and residential electricity consumption: Evidence from Chinese households," Energy Economics, Elsevier, vol. 107(C).
    16. Costanzo, Vincenzo & Yao, Runming & Xu, Tiantian & Xiong, Jie & Zhang, Qiulei & Li, Baizhan, 2019. "Natural ventilation potential for residential buildings in a densely built-up and highly polluted environment. A case study," Renewable Energy, Elsevier, vol. 138(C), pages 340-353.
    17. Boya Zhou & Shaojun Zhang & Ye Wu & Wenwei Ke & Xiaoyi He & Jiming Hao, 2018. "Energy-saving benefits from plug-in hybrid electric vehicles: perspectives based on real-world measurements," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 23(5), pages 735-756, June.
    18. Liwei Wen & Kyosuke Hiyama, 2018. "Target Air Change Rate and Natural Ventilation Potential Maps for Assisting with Natural Ventilation Design During Early Design Stage in China," Sustainability, MDPI, vol. 10(5), pages 1-16, May.
    19. Wei Xue & Qingming Zhan & Qi Zhang & Zhonghua Wu, 2019. "Spatiotemporal Variations of Particulate and Gaseous Pollutants and Their Relations to Meteorological Parameters: The Case of Xiangyang, China," IJERPH, MDPI, vol. 17(1), pages 1-23, December.
    20. Martins, Nuno R. & Carrilho da Graça, Guilherme, 2017. "Impact of outdoor PM2.5 on natural ventilation usability in California’s nondomestic buildings," Applied Energy, Elsevier, vol. 189(C), pages 711-724.

    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:eee:appene:v:253:y:2019:i:c:94. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.