Abstract
Intensive use of heating, ventilation and air conditioning (HVAC) systems in buildings entails an analysis and monitoring of their efficiency. Cooling systems are key facilities in large buildings, and particularly critical in hospitals, where chilled water production is needed as an auxiliary resource for a large number of devices. A chiller plant is often composed of several HVAC units running at the same time, being impossible to assess the individual cooling production and efficiency, since a sensor is seldom installed due to the high cost. We propose a virtual sensor that provides an estimation of the cooling production, based on a deep learning architecture that features a 2D CNN (Convolutional Neural Network) to capture relevant features on two-way matrix arrangements of chiller data involving thermodynamic variables and the refrigeration circuits of the chiller unit. Our approach has been tested on an air-cooled chiller in the chiller plant at a hospital, and compared to other state-of-the-art methods using 10-fold cross-validation. Our results report the lowest errors among the tested methods and include a comparison of the true and estimated cooling production and efficiency for a period of several days.
This work was supported in part by the Spanish Ministerio de Ciencia e Innovacion (MICINN) and the European FEDER funds under project CICYT DPI2015-69891-C2-1-R/2-R.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Alves, O., Monteiro, E., Brito, P., Romano, P.: Measurement and classification of energy efficiency in HVAC systems. Energy Build. 130, 408–419 (2016). https://doi.org/10.1016/j.enbuild.2016.08.070
Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995). www.scopus.com, cited By (since 1996): 4606
Escobedo-Trujillo, B., Colorado, D., Rivera, W., Alaffita-Hernández, F.: Neural network and polynomial model to improve the coefficient of performance prediction for solar intermittent refrigeration system. Sol. Energy 129, 28–37 (2016). https://doi.org/10.1016/j.solener.2016.01.041
Fan, C., Xiao, F., Zhao, Y.: A short-term building cooling load prediction method using deep learning algorithms. Appl. Energy 195, 222–233 (2017). https://doi.org/10.1016/j.apenergy.2017.03.064
Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24(6), 381–395 (1981)
Fu, G.: Deep belief network based ensemble approach for cooling load forecasting of air-conditioning system. Energy 148, 269–282 (2018). https://doi.org/10.1016/j.energy.2018.01.180
Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge (2016). http://www.deeplearningbook.org
Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning, 2nd edn. Springer, New York (2009). https://doi.org/10.1007/978-0-387-84858-7
Haykin, S.S.: Neural Networks and Learning Machines, 3rd edn. Pearson Education, London (2009)
Klein, S.A.: Design considerations for refrigeration cycles. In: International Refrigeration and Air Conditioning Conference, vol. 190, pp. 511–519. Purdue e-Pubs (1992). http://docs.lib.purdue.edu/iracc/190
Kusiak, A., Li, M., Zheng, H.: Virtual models of indoor-air-quality sensors. Appl. Energy 87(6), 2087–2094 (2010). https://doi.org/10.1016/j.apenergy.2009.12.008
LeCun, Y., Bengio, Y.: Convolutional networks for images, speech, and time series. In: The Handbook of Brain Theory and Neural Networks, pp. 255–258. MIT Press, Cambridge, MA, USA (1998)
Li, H., Yu, D., Braun, J.E.: A review of virtual sensing technology and application in building systems. HVAC&R Res. 17(5), 619–645 (2011). https://doi.org/10.1080/10789669.2011.573051
Mcdonald, E., Zmeureanu, R.: Virtual flow meter to estimate the water flow rates in chillers. ASHRAE Trans. 120, 200–208 (2014)
McDonald, E., Zmeureanu, R.: Development and testing of a virtual flow meter tool to monitor the performance of cooling plants. Energy Procedia 78, 1129–1134 (2015). https://doi.org/10.1016/j.egypro.2015.11.071
Pérez-Lombard, L., Ortiz, J., Pout, C.: A review on buildings energy consumption information. Energy Build. 40(3), 394–398 (2008). https://doi.org/10.1016/j.enbuild.2007.03.007
Saidur, R., Hasanuzzaman, M., Mahlia, T., Rahim, N., Mohammed, H.: Chillers energy consumption, energy savings and emission analysis in an institutional buildings. Energy 36(8), 5233–5238 (2011). https://doi.org/10.1016/j.energy.2011.06.027. PRES 2010
Wang, H.: Water flow rate models based on the pipe resistance and pressure difference in multiple parallel chiller systems. Energy Build. 75, 181–188 (2014). https://doi.org/10.1016/j.enbuild.2014.02.017
Zhao, X., Yang, M., Li, H.: Development, evaluation and validation of a robust virtual sensing method for determining water flow rate in chillers. HVAC&R Res. 18(5), 874–889 (2012). https://doi.org/10.1080/10789669.2012.667036
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Alonso, S., Morán, A., Pérez, D., Reguera, P., Díaz, I., Domínguez, M. (2019). Virtual Sensor Based on a Deep Learning Approach for Estimating Efficiency in Chillers. In: Macintyre, J., Iliadis, L., Maglogiannis, I., Jayne, C. (eds) Engineering Applications of Neural Networks. EANN 2019. Communications in Computer and Information Science, vol 1000. Springer, Cham. https://doi.org/10.1007/978-3-030-20257-6_26
Download citation
DOI: https://doi.org/10.1007/978-3-030-20257-6_26
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-20256-9
Online ISBN: 978-3-030-20257-6
eBook Packages: Computer ScienceComputer Science (R0)