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Título
Virtual Sensor Based on a Deep Learning Approach for Estimating Efficiency in Chillers
Autor
Facultad/Centro
Área de conocimiento
Datos de la obra
International Conference on Engineering Applications of Neural Networks, 2017
Editor
Springer
Fecha
2017
ISSN
1865-0929
Abstract
Intensive use of heating, ventilation and air conditioning
(HVAC) systems in buildings entails an analysis and monitoring of their
e ciency. Cooling systems are key facilities in large buildings, and par-
ticularly 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, be-
ing impossible to assess the individual cooling production and e ciency,
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 (Convolu-
tional 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 re-
sults report the lowest errors among the tested methods and include a
comparison of the true and estimated cooling production and e ciency
for a period of several days
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