Bio-inspired model of ground temperature behavior on the horizontal geothermal exchanger of an installation based on a heat pump

Casteleiro‐Roca, José‐Luis; Calvo-Rolle, José Luís; Meizoso-López, María del Carmen; Piñón-Pazos, A.; Gómez, Benigno Antonio Rodríguez

doi:10.1016/j.neucom.2014.02.075

Cited by 29 publications

(6 citation statements)

References 31 publications

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“…The Multilayer Perceptron (MLP) is one of the most frequently used shallow neural network architectures. The good performance of this kind of artificial neural network has been proven in similar works such as [31,32,33]. Previous research [34] proved how this technique is capable of providing satisfactory results in the context of much larger amounts of data than those used in the case of study.…”

Section: Fault Detection and Recovery (Fdr) Approach—used Techniquesmentioning

confidence: 79%

A Fault Detection System for a Geothermal Heat Exchanger Sensor Based on Intelligent Techniques

Aláiz‐Moretón

Castejón-Limas

Casteleiro‐Roca

et al. 2019

Sensors

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This paper proposes a methodology for dealing with an issue of crucial practical importance in real engineering systems such as fault detection and recovery of a sensor. The main goal is to define a strategy to identify a malfunctioning sensor and to establish the correct measurement value in those cases. As study case, we use the data collected from a geothermal heat exchanger installed as part of the heat pump installation in a bioclimatic house. The sensor behaviour is modeled by using six different machine learning techniques: Random decision forests, gradient boosting, extremely randomized trees, adaptive boosting, k-nearest neighbors, and shallow neural networks. The achieved results suggest that this methodology is a very satisfactory solution for this kind of systems.

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Section: Fault Detection and Recovery (Fdr) Approach—used Techniquesmentioning

confidence: 79%

A Fault Detection System for a Geothermal Heat Exchanger Sensor Based on Intelligent Techniques

Aláiz‐Moretón

Castejón-Limas

Casteleiro‐Roca

et al. 2019

Sensors

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“…The algorithms based on multiple regression analysis are accepted regression methods used in several applications [32][33][34][35][36][37]. Some previous works have shown the use of these methods despite its low performance [33,[38][39][40]. In this paper, to overcome this limitation, we propose to use hybrid intelligent system to accomplish the regression task, more specifically, an artificial neural network (ANN) hybrid system as the ones used in [41][42][43][44][45][46], since ANNs allow obtaining simple and very accurate nonlinear models [47][48][49][50].…”

Section: Fuel Outputmentioning

confidence: 99%

Fuel Cell Output Current Prediction with a Hybrid Intelligent System

et al. 2019

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A fuel cell is a complex system, which produces electricity through an electrochemical reaction. For the formal application of control strategies on a fuel cell, it is very important to have a precise dynamic model of it. In this paper, a dynamic model of a real hydrogen fuel cell is obtained to predict its response. The data used in this paper to obtain the model have been acquired from a real fuel cell subjected to different load patterns by means of a programmable electronic load. Using this data, a nonlinear model based on a hybrid intelligent system is obtained. This hybrid model uses artificial neural networks to predict the output current of the fuel cell in a very precise way. The use of a hybrid scheme improves the performance of neural networks reducing to half the mean squared error obtained for a global model of the fuel cell.

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“…Both configurations, vertical and horizontal have their own operation problems, but the horizontal has more disadvantages than the other, among others because the exchanger is closer to the ground surface [38,49]. Due to the proximity to the surface, the weather has influence over the exchanger and the efficiency could be lower [51,39,14]; even that, there are some studies like [16] that show the effect of the exchanger over the ground temperature. For the same reason, the installation may be damaged due to different reasons like crushing, perforations, and so on [4].…”

Section: Heat Pump Systemsmentioning

confidence: 99%

An intelligent fault detection system for a heat pump installation based on a geothermal heat exchanger

Casteleiro‐Roca

Quintián

Calvo-Rolle

et al. 2016

Journal of Applied Logic

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The heat pump with geothermal exchanger is one of the best methods to heat up a building. The heat exchanger is an element with high probability of failure due to the fact that it is an outside construction and also due to its size. In the present study, a novel intelligent system was designed to detect faults on this type of heating equipment. The novel approach has been successfully empirically tested under a real dataset obtained during measurements of one year. It was based on classification techniques with the aim of detecting failures in real time. Then, the model was validated and verified over the building; it obtained good results in all the operating conditions ranges.

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Bio-inspired model of ground temperature behavior on the horizontal geothermal exchanger of an installation based on a heat pump

Cited by 29 publications

References 31 publications

A Fault Detection System for a Geothermal Heat Exchanger Sensor Based on Intelligent Techniques

A Fault Detection System for a Geothermal Heat Exchanger Sensor Based on Intelligent Techniques

Fuel Cell Output Current Prediction with a Hybrid Intelligent System

An intelligent fault detection system for a heat pump installation based on a geothermal heat exchanger

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