372
Views
1
CrossRef citations to date
0
Altmetric
Topical Section: Artificial Intelligence in Smart Buildings

User-friendly fault detection method for building chilled water flowmeters: Field data validation

, , &

References

  • Ahmad, M. W., M. Mourshed, D. Mundow, M. Sisinni, and Y. Rezgui. 2016. Building energy metering and environmental monitoring – A state-of-the-art review and directions for future research. Energy and Buildings 120:85–102. doi:10.1016/j.enbuild.2016.03.059
  • Ahmad, M. W., M. Mourshed, and Y. Rezgui. 2017. Trees vs Neurons: Comparison between random forest and ANN for high-resolution prediction of building energy consumption. Energy and Buildings 147:77–89. doi:10.1016/j.enbuild.2017.04.038
  • ASHRAE Standards Committee. 2002. ASHRAE guideline 14. Measurement of energy and demand savings. Atlanta: ASHRAE.
  • Bae, Y., S. Bhattacharya, B. Cui, S. Lee, Y. Li, L. Zhang, P. Im, V. Adetola, D. Vrabie, M. Leach, et al. 2021. Sensor impacts on building and HVAC controls: A critical review for building energy performance. Advances in Applied Energy 4:100068. doi:10.1016/j.adapen.2021.100068
  • Bai, Y., Q. Ren, and K. Ma. 2013. Modeling and generalized predictive control for chilled water in central air-conditioning system. Computer & Information Science 6 (4):25–36.
  • Bernard, S., L. Heutte, and S. Adam. 2009. Influence of hyperparameters on random forest accuracy. Proceedings of the 8th International Workshop on Multiple Classifier Systems.
  • Blanch, J., V. Puig, J. Saludes, and J. Quevedo. 2009. ARIMA models for data consistency of flowmeters in water distribution networks. IFAC Proceedings Volumes 42 (8):480–5. doi:10.3182/20090630-4-ES-2003.00080
  • Breiman, L. 2001. Random forests. Machine Learning 45 (1):5–32. doi:10.1023/A:1010933404324
  • Chang, Y. C. 2004. A novel energy conservation method—Optimal chiller loading. Electric Power Systems Research 69 (2–3):221–6. doi:10.1016/j.epsr.2003.10.012
  • Chaudhuri, T., D. Zhai, Y. C. Soh, H. Li, and L. Xie. 2018. Random forest based thermal comfort prediction from gender-specific physiological parameters using wearable sensing technology. Energy and Buildings 166:391–406. doi:10.1016/j.enbuild.2018.02.035
  • Cui, J. 2005. A robust fault detection and diagnosis strategy for centrifugal chillers. Ph.D., Hong Kong Polytechnic University.
  • Cui, J., and S. Wang. 2005. A model-based online fault detection and diagnosis strategy for centrifugal chiller systems. International Journal of Thermal Sciences 44 (10):986–99. doi:10.1016/j.ijthermalsci.2005.03.004
  • Davies, D. L., and D. W. Bouldin. 1979. A cluster separation measure. IEEE Transactions on Pattern Analysis and Machine Intelligence PAMI-1 (2):224–7. doi:10.1109/TPAMI.1979.4766909
  • Díaz-Uriarte, R., and S. Alvarez de Andrés. 2006. Gene selection and classification of microarray data using random forest. BMC Bioinformatics 7 (1):3.
  • Du, Z., and X. Jin. 2007. Detection and diagnosis for sensor fault in HVAC systems. Energy Conversion and Management 48 (3):693–702. doi:10.1016/j.enconman.2006.09.023
  • Fang, H., R. Sharma, and R. Patil. 2014. Optimal sensor and actuator deployment for HVAC control system design.
  • Fernandez-Delgado, M., E. Cernadas, S. Barro, and D. Amorim. 2014. Do we need hundreds of classifiers to solve real world classification problems? Journal of Machine Learning Research 15:3133–81.
  • Fu, Y., Z. Li, F. Feng, and P. Xu. 2016. Data-quality detection and recovery for building energy management and control systems: Case study on submetering. Science and Technology for the Built Environment 22 (6):798–809. doi:10.1080/23744731.2016.1195658
  • Gao, D. C., S. Wang, K. Shan, and C. Yan. 2016. A system-level fault detection and diagnosis method for low delta-T syndrome in the complex HVAC systems. Applied Energy 164:1028–38. doi:10.1016/j.apenergy.2015.02.025
  • Géron, A. 2018. Hands-on machine learning with scikit-learn and tensorflow. New York, NY: O'Reilly.
  • Gu, J., P. Xu, Z. Pang, Y. Chen, Y. Ji, and Z. Chen. 2018. Extracting typical occupancy data of different buildings from mobile positioning data. Energy and Buildings 180:135–45.
  • Han, H., B. Gu, J. Kang, and Z. R. Li. 2011. Study on a hybrid SVM model for chiller FDD applications. Applied Thermal Engineering 31 (4):582–92. doi:10.1016/j.applthermaleng.2010.10.021
  • Han, H., Z. Zhang, X. Cui, and Q. Meng. 2020. Ensemble learning with member optimization for fault diagnosis of a building energy system. Energy and Buildings 226:110351. doi:10.1016/j.enbuild.2020.110351
  • Hotelling, H. 1931. The generalization of student's ratio. The Annals of Mathematical Statistics 2 (3):360–78. doi:10.1214/aoms/1177732979
  • Hou, J., P. Xu, X. Lu, Z. Pang, Y. Chu, and G. Huang. 2018. Implementation of expansion planning in existing district energy system: A case study in China. Applied Energy 211:269–81. doi:10.1016/j.apenergy.2017.10.118
  • Huang, G., and Z. Li. 2014. Stochastic chiller sequencing control for multiple-chiller plants. IEEE International Conference on Automation Science and Engineering.
  • Huang, S., W. Zuo, and M. D. Sohn. 2017. Improved cooling tower control of legacy chiller plants by optimizing the condenser water set point. Building and Environment 111:33–46. doi:10.1016/j.buildenv.2016.10.011
  • Kim, J., Y. Zhou, S. Schiavon, P. Raftery, and G. Brager. 2018. Personal comfort models: Predicting individuals' thermal preference using occupant heating and cooling behavior and machine learning. Building and Environment 129:96–106. doi:10.1016/j.buildenv.2017.12.011
  • Kim, M.-H., and J.-W. Jeong. 2017. Experimental verification of a virtual water flowmeter applicable to air conditioning systems. Energy and Buildings 155:425–38. doi:10.1016/j.enbuild.2017.09.050
  • Levenhagen, J. I. 1999. HVAC control system design diagrams. New York, USA: McGraw-Hill.
  • Li, D., C. C. Menassa, and V. R. Kamat. 2017. Personalized human comfort in indoor building environments under diverse conditioning modes. Building and Environment 126:304–17. doi:10.1016/j.buildenv.2017.10.004
  • Li, G., Y. Zheng, J. Liu, Z. Zhou, C. Xu, X. Fang, and Q. Yao. 2021. An improved stacking ensemble learning-based sensor fault detection method for building energy systems using fault-discrimination information. Journal of Building Engineering 43:102812. doi:10.1016/j.jobe.2021.102812
  • Li, W., P. Xu, X. Lu, H. Wang, and Z. Pang. 2016. Electricity demand response in China: Status, feasible market schemes and pilots. Energy 114:981–94. doi:10.1016/j.energy.2016.08.081
  • Li, Z., and G. Huang. 2013. Preventive approach to determine sensor importance and maintenance requirements. Automation in Construction 31 (3):307–12.
  • Liaw, A., and M. Wiener. 2001. Classification and regression by randomForest.
  • Liu, C. H. B., B. P. Chamberlain, D. A. Little, and Â. Cardoso. 2017. Generalising random forest parameter optimisation to include stability and cost. In Machine learning and knowledge discovery in databases. Cham: Springer International Publishing.
  • Luo, X. J., K. F. Fong, Y. J. Sun, and M. K. H. Leung. 2019. Development of clustering-based sensor fault detection and diagnosis strategy for chilled water system. Energy and Buildings 186:17–36. doi:10.1016/j.enbuild.2019.01.006
  • Ma, Z., and S. Wang. 2011. Online fault detection and robust control of condenser cooling water systems in building central chiller plants. Energy and Buildings 43 (1):153–65. doi:10.1016/j.enbuild.2010.09.003
  • Maasoumy, M., M. Razmara, M. Shahbakhti, and A. S. Vincentelli. 2014. Handling model uncertainty in model predictive control for energy efficient buildings. Energy and Buildings 77:377–92. doi:10.1016/j.enbuild.2014.03.057
  • Manjarres, D., A. Mera, E. Perea, A. Lejarazu, and S. Gil-Lopez. 2017. An energy-efficient predictive control for HVAC systems applied to tertiary buildings based on regression techniques. Energy and Buildings 152:409–17. doi:10.1016/j.enbuild.2017.07.056
  • Montgomery, D. C., and G. C. Runger. 2003. Applied statistics and probability for engineers. 4th ed. USA: Wiley.
  • O'Driscoll, E., and G. E. O'Donnell. 2013. Industrial power and energy metering - A state-of-the-art review. Journal of Cleaner Production 41 (1):53–64. doi:10.1016/j.jclepro.2012.09.046
  • Ole Fanger, P., and J. Toftum. 2002. Extension of the PMV model to non-air-conditioned buildings in warm climates. Energy and Buildings 34 (6):533–6. doi:10.1016/S0378-7788(02)00003-8
  • Pang, Z., P. Xu, and Z. D. O’Neill. 2018. Application of mobile-internet-based occupancy data in building energy model calibration: A case study. 2018 ASHRAE Annual Meeting. Houston, TX. doi:10.1016/j.buildenv.2018.05.030
  • Pedregosa, F., G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, et al. 2011. Scikit-learn: Machine learning in Python. Journal of Machine Learning Research 1 (12):2825–30.
  • Pérez-Lombard, L., J. Ortiz, and C. Pout. 2008. A review on buildings energy consumption information. Energy and Buildings 40 (3):394–8. doi:10.1016/j.enbuild.2007.03.007
  • Purdon, S., B. Kusy, R. Jurdak, and G. Challen. 2013. Model-free HVAC control using occupant feedback. IEEE Conference on Local Computer Networks Workshops.
  • Qiu, S., Z. Li, and Z. Li. 2020. User-friendly fault detection method for building chilled water flowmeters. 12th International Conference on Applied Energy 2020 (ICAE 2020), Thailand/Virtual.
  • Qiu, S., Z. Li, Z. Pang, W. Zhang, and Z. Li. 2018. A quick auto-calibration approach based on normative energy models. Energy and Buildings 172:35–46. doi:10.1016/j.enbuild.2018.04.053
  • Rousseeuw, P. J. 1987. Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics 20:53–65. doi:10.1016/0377-0427(87)90125-7
  • Srivastava, T. 2015. Tuning the parameters of your Random Forest model. https://www.analyticsvidhya.com/blog/2015/06/tuning-random-forest-model/.
  • Sun, Y., S. Wang, and G. Huang. 2010. Online sensor fault diagnosis for robust chiller sequencing control. International Journal of Thermal Sciences 49 (3):589–602. doi:10.1016/j.ijthermalsci.2009.10.003
  • Surhone, L. M., M. T. Tennoe, S. F. Henssonow, and L. Breiman. 2010. Random Forest. Machine Learning 45 (1):5–32.
  • Svetnik, V., A. Liaw, C. Tong, and T. Wang. 2004. Application of Breiman’s Random Forest to Modeling Structure-Activity Relationships of Pharmaceutical Molecules. Multiple Classifier Systems. Berlin, Heidelberg: Springer.
  • Taheri, S., and A. Razban. 2021. Learning-based CO2 concentration prediction: Application to indoor air quality control using demand-controlled ventilation. Building and Environment 205:108164. doi:10.1016/j.buildenv.2021.108164
  • Valladares, W., M. Galindo, J. Gutiérrez, W.-C. Wu, K.-K. Liao, J.-C. Liao, K.-C. Lu, and C.-C. Wang. 2019. Energy optimization associated with thermal comfort and indoor air control via a deep reinforcement learning algorithm. Building and Environment 155:105–17. doi:10.1016/j.buildenv.2019.03.038
  • Wainberg, M., B. Alipanahi, and B. J. Frey. 2016. Are random forests truly the best classifiers? JMLR.org.
  • Wang, G., K. Kiamehr, and L. Song. 2016. Development of a virtual pump water flow meter with a flow rate function of motor power and pump head. Energy and Buildings 117:63–70. doi:10.1016/j.enbuild.2016.02.003
  • Wang, H., Y. Chen, C. W. H. Chan, J. Qin, and J. Wang. 2012. Online model-based fault detection and diagnosis strategy for VAV air handling units. Energy and Buildings 55:252–63. doi:10.1016/j.enbuild.2012.08.016
  • Wang, J., G. Huang, Y. Sun, and X. Liu. 2016. Event-driven optimization of complex HVAC systems. Energy and Buildings 133:79–87. doi:10.1016/j.enbuild.2016.09.049
  • Wang, S., and F. Xiao. 2004. AHU sensor fault diagnosis using principal component analysis method. Energy and Buildings 36 (2):147–60. doi:10.1016/j.enbuild.2003.10.002
  • Wang, S., and J.-B. Wang. 1999. Law-based sensor fault diagnosis and validation for building air-conditioning systems. HVAC&R Research 5 (4):353–80. doi:10.1080/10789669.1999.10391243
  • Wang, S., and J.-B. Wang. 2002. Automatic sensor evaluation in BMS commissioning of building refrigeration systems. Automation in Construction 11 (1):59–73. doi:10.1016/S0926-5805(01)00050-4
  • Wang, S., and Y. Chen. 2004. Sensor validation and reconstruction for building central chilling systems based on PCA. Energy Conversion and Management 45 (5):673–95. doi:10.1016/S0196-8904(03)00180-8
  • Wang, S., and Z. Ma. 2008. Supervisory and optimal control of building HVAC systems: A review. HVAC&R Research 14 (1):3–32. doi:10.1080/10789669.2008.10390991
  • Wang, S., J. Wang, and J. Burnett. 2001. Validating BMS sensors for chiller condition monitoring. Transactions of the Institute of Measurement and Control 23 (4):201–25. doi:10.1177/014233120102300401
  • Wang, Z., E. Andiroglu, G. Wang, and L. Song. 2019. Accuracy improvement of virtual pump water flow meters using calibrated characteristics curves at various frequencies. Energy and Buildings 191:143–50. doi:10.1016/j.enbuild.2019.03.021
  • Witten, I. H., and E. Frank. 2002. Data mining: Practical machine learning tools and techniques with Java implementations. ACM SIGMOD Record 31 (1):76–7. doi:10.1145/507338.507355
  • Yan, K., W. Shen, T. Mulumba, and A. Afshari. 2014. ARX model based fault detection and diagnosis for chillers using support vector machines. Energy and Buildings 81:287–95. doi:10.1016/j.enbuild.2014.05.049
  • Yan, R., Z. Ma, G. Kokogiannakis, and Y. Zhao. 2016. A sensor fault detection strategy for air handling units using cluster analysis. Automation in Construction 70:77–88. doi:10.1016/j.autcon.2016.06.005
  • Yao, Y., and J. Chen. 2010. Global optimization of a central air-conditioning system using decomposition–coordination method. Energy and Buildings 42 (5):570–83. doi:10.1016/j.enbuild.2009.10.027
  • Yao, Y., Z. Lian, Z. Hou, and X. Zhou. 2004. Optimal operation of a large cooling system based on an empirical model. Applied Thermal Engineering 24 (16):2303–21. doi:10.1016/j.applthermaleng.2004.03.006
  • Zhang, Z., H. Han, X. Cui, and Y. Fan. 2020. Novel application of multi-model ensemble learning for fault diagnosis in refrigeration systems. Applied Thermal Engineering 164:114516. doi:10.1016/j.applthermaleng.2019.114516
  • Zhao, X., M. Yang, and H. Li. 2014. Field implementation and evaluation of a decoupling-based fault detection and diagnostic method for chillers. Energy and Buildings 72:419–30. doi:10.1016/j.enbuild.2014.01.003
  • Zhou, Z.-H., J. Wu, and W. Tang. 2002. Ensembling neural networks: Many could be better than all. Artificial Intelligence 137 (1–2):239–63. doi:10.1016/S000s3702(02)00190-X
  • Zou, Z., X. Yu, and S. Ergan. 2020. Towards optimal control of air handling units using deep reinforcement learning and recurrent neural network. Building and Environment 168:106535. doi:10.1016/j.buildenv.2019.106535

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.