Abstract
Heating, Ventilation, and Air Conditioning (HVAC) systems are generally built in a modular manner, comprising several identical subsystems in order to achieve their nominal capacity. These parallel subsystems and elements should have the same behavior and, therefore, differences between them can reveal failures and inefficiency in the system. The complexity in HVAC systems comes from the number of variables involved in these processes. For that reason, dimensionality reduction techniques can be a useful approach to reduce the complexity of the HVAC data and study their operation. However, for most of these techniques, it is not possible to project new data without retraining the projection and, as a result, it is not possible to easily compare several projections. In this paper, a method based on deep autoencoders is used to create a reference model with a HVAC system and new data is projected using this model to be able to compare them. The proposed approach is applied to real data from a chiller with 3 identical compressors at the Hospital of León.
This work was supported in part by the Spanish Ministerio de Ciencia e Innovación (MICINN) and the European FEDER funds under project CICYT DPI2015-69891-C2-1-R/2-R.
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Morán, A., Alonso, S., Prada, M.A., Fuertes, J.J., Díaz, I., Domínguez, M. (2017). Analysis of Parallel Process in HVAC Systems Using Deep Autoencoders. In: Boracchi, G., Iliadis, L., Jayne, C., Likas, A. (eds) Engineering Applications of Neural Networks. EANN 2017. Communications in Computer and Information Science, vol 744. Springer, Cham. https://doi.org/10.1007/978-3-319-65172-9_2
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