RT info:eu-repo/semantics/article
T1 Estimating cooling production and monitoring efficiency in chillers using a soft sensor
A1 Alonso Castro, Serafín
A1 Morán Álvarez, Antonio
A1 Pérez López, Daniel
A1 Prada Medrano, Miguel Ángel
A1 Diaz Blanco, Ignacio
A1 Domínguez González, Manuel
A2 Ingenieria de Sistemas y Automatica
K1 Informática
K1 Ingeniería industrial
K1 HVAC systems
K1 Chillers
K1 Efficiency
K1 Cooling production
K1 Soft sensor
K1 Deep learning
K1 2213.06 Bajas Temperaturas
K1 3322.02 Generación de Energía
AB [EN] Intensive use of heating, ventilation and air conditioning systems in buildings entails monitoring their efficiency. Moreover, cooling systems are key facilities in large buildings and can account up to 44% of the energy consumption. Therefore, monitoring efficiency in chillers is crucial and, for that reason, a sensor to measure the cooling production is required. However, manufacturers rarely install it in the chiller due to its cost. In this paper, we propose a methodology to build a soft sensor that provides an estimation of cooling production and enables monitoring the chiller efficiency. The proposed soft sensor uses independent variables (internal states of the chiller and electric power) and can take advantage of current or past observations of those independent variables. Six methods (from linear approaches to deep learning ones) are proposed to develop the model for the soft sensor, capturing relevant features on the structure of data (involving time, thermodynamic and electric variables and the number of refrigeration circuits). Our approach has been tested on two different chillers (large water-cooled and smaller air-cooled chillers) installed at the Hospital of León. The methods to implement the soft sensor are assessed according to three metrics (MAE, MAPE and R²). In addition to the comparison of methods, the results also include the estimation of cooling production (and the comparison of the true and estimated values) and monitoring the COP indicator for a period of several days and for both chillers.
PB Springer
SN 0941-0643
LK https://hdl.handle.net/10612/17878
UL https://hdl.handle.net/10612/17878
NO Alonso, S., Moran, A., Pérez, D., Prada, M. A., Diaz, I., & Domínguez, M. (2020). Estimating cooling production and monitoring efficiency in chillers using a soft sensor. Neural Computing and Applications, 32(23), 17291-17308.
DS BULERIA. Repositorio Institucional de la Universidad de León
RD 20-may-2024