Abstract
Association rule mining has been applied to reveal variable relations from numerous operational data in buildings. However, there is a lack of effective methods to take full advantage of the discovered relations. This study proposes a real-time data analysis method for diagnosing building operational problems based on the discovered relations. In this method, the historical data are explored by the association rule mining to generate raw association rules. The abnormal and normal rules are extracted manually to build a rule base. The rule base is then used to analyze the real-time measurements of the relevant variables in the extracted rules. Operational problems are detected if the measurements of the relevant variables match with an abnormal rule or break all the related normal rules. Evaluations are made using the operational data collected from the chiller plant of a commercial building located in Shenzhen, China. Results show that the proposed method can detect operational problems effectively.
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References
Katipamula, S., Brambley, M.R.: Review article: methods for fault detection, diagnostics, and prognostics for building systems—a review, part I. HVAC&R Res. 11, 3–25 (2005)
Yu, Z., Haghighat, F., Fung, B.C.M., et al.: A methodology for identifying and improving occupant behavior in residential buildings. Energy 36, 6596–6608 (2011)
Yu, Z., Haghighat, F., Fung, B.C.M., et al.: A novel methodology for knowledge discovery through mining associations between building operational data. Energy Build. 47, 430–440 (2012)
Cabrera, D.F.M., Zareipour, H.: Data association mining for identifying lighting energy waste patterns in educational institutes. Energy Build. 62, 210–216 (2013)
Xiao, F., Fan, C.: Data mining in building automation system for improving building operational performance. Energy Build. 75, 109–118 (2014)
Fan, C., Xiao, F., Yan, C.: A framework for knowledge discovery in massive building automation data and its application in building diagnostics. Autom. Constr. 50, 81–90 (2015)
Peña, M., Biscarri, F., Guerrero, J.I., et al.: Rule-based system to detect energy efficiency anomalies in smart buildings, a data mining approach. Expert Syst. Appl. 56, 242–255 (2016)
Schein, J., Bushby, S.T., Castro, N.S., et al.: A rule-based fault detection method for air handling units. Energy Build. 38, 1485–1492 (2006)
Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. In: Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, pp. 1–12. ACM, New York (2000)
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This study is supported by the National Nature Science Foundation of China (Number 51706197).
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Zhang, C., Zhao, Y., Zhang, X. (2020). An Association Rule-Based Online Data Analysis Method for Improving Building Energy Efficiency. In: Wang, Z., Zhu, Y., Wang, F., Wang, P., Shen, C., Liu, J. (eds) Proceedings of the 11th International Symposium on Heating, Ventilation and Air Conditioning (ISHVAC 2019). ISHVAC 2019. Environmental Science and Engineering(). Springer, Singapore. https://doi.org/10.1007/978-981-13-9524-6_40
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DOI: https://doi.org/10.1007/978-981-13-9524-6_40
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