Estimating cooling production and monitoring efficiency in chillers using a soft sensor
Author:
Funder
Ministerio de Ciencia, Innovación y Universidades
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Software
Link
https://link.springer.com/content/pdf/10.1007/s00521-020-05165-2.pdf
Reference58 articles.
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3. 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. Springer International Publishing, Cham, pp 307–319
4. Alves O, Monteiro E, Brito P, Romano P (2016) Measurement and classification of energy efficiency in HVAC systems. Energy Build 130:408–419. https://doi.org/10.1016/j.enbuild.2016.08.070
5. Bechtler H, Browne M, Bansal P, Kecman V (2001) New approach to dynamic modelling of vapour-compression liquid chillers: artificial neural networks. Appl Therm Eng 21(9):941–953. https://doi.org/10.1016/S1359-4311(00)00093-4
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