Virtual sensor-assisted in situ sensor calibration in operational HVAC systems

Choi, Youngwoong; Yoon, Sung‐Min

doi:10.1016/j.buildenv.2020.107079

Cited by 38 publications

(11 citation statements)

References 34 publications

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“…También pueden ser empleados para la supervisión de redes IoT, donde los sensores virtuales predicen cuando un sensor no funciona correctamente [14]. En otros ámbitos diferentes como la automatización de edificios, se han propuesto sensores virtuales basados en datos o bien propuestas de caja gris [15].…”

Section: Estado Del Arteunclassified

Control de caudal con un sensor virtual basado en técnicas de deep learning

Herbón¹,

Ossorio²,

Mateos³

et al. 2022

XLIII Jornadas De Automática: Libro De Actas: 7, 8 Y 9 De Septiembre De 2022, Logroño (La Rioja)

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En el ámbito del control industrial, resulta sumamente interesante el empleo de sensores virtuales en los casos en los que no se puede tener acceso a la variable física a controlar, bien sea porque el sensor real es costoso, no puede ser instalado o se encuentra averiado. En este artículo se propone la implementación de un lazo de control del caudal de una planta piloto industrial, comparando el desempeño de este lazo cuando se emplea el caudalímetro real y cuando se emplea un sensor virtual de caudal. El sensor virtual utilizado se ha desarrollado empleando una red neuronal recurrente.

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Section: Estado Del Arteunclassified

Control de caudal con un sensor virtual basado en técnicas de deep learning

Herbón¹,

Ossorio²,

Mateos³

et al. 2022

XLIII Jornadas De Automática: Libro De Actas: 7, 8 Y 9 De Septiembre De 2022, Logroño (La Rioja)

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“…Several studies focused on the application of virtual sensors in building energy systems have been conducted [4][5][6][7][8][9]. Qian et al [6] suggested virtual power consumption sensors for variable refrigerant flow (VRF) systems using a multiple linear regression model developed with laboratory datasets.…”

Section: Literature Review and Limitationsmentioning

confidence: 99%

“…Previous studies typically involved laboratory experimental setups (because it is difficult to obtain various operational datasets in a real DH substation or to perform sensing in actual buildings). 4. Key sensors for system control are usually installed or managed in real systems.…”

mentioning

confidence: 99%

Virtual Sensors for Estimating District Heating Energy Consumption under Sensor Absences in a Residential Building

et al. 2020

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District heating (DH) is an energy efficient building heating system that entails low primary energy consumption and reduced environmental impact. The estimation of the required heating load provides information for operators to control district heating systems (DHSs) efficiently. It also yields historical datasets for intelligent management applications. Based on the existing virtual sensor capabilities to estimate physical variables, performance, etc., and to detect the anomaly detection in building energy systems, this paper proposes a virtual sensor-based method for the estimation of DH energy consumption in a residential building. Practical issues, including sensor absences and limited datasets corresponding to actual buildings, were also analyzed to improve the applicability of virtual sensors in a building. According to certain virtual sensor development processes, model-driven, data-driven, and grey-box virtual sensors were developed and compared in a case study. The grey-box virtual sensor surpassed the capabilities of the other virtual sensors, particularly for operation patterns corresponding to low heating, which were different from those in the training dataset; notably, a 16% improvement was observed in the accuracy exhibited by the grey-box virtual sensor, as compared to that of the data-driven virtual sensor. The former sensor accounted for a significantly wider DHS operation range by overcoming training data dependency when estimating the actual DH energy consumption. Finally, the proposed virtual sensors can be applied for continuous commissioning, monitoring, and fault detection in the building, since they are developed based on the DH variables at the demand side.

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“…The backup virtual sensor can be regarded as a working sensor in the cyber world. Based on the residuals between physical and virtual measurements, IBVS can be used for the following purposes: (1) the FDD of building energy systems [14], (2) virtualization of malfunctioning sensors [15], (3) in situ calibration of physical sensors [16,17], and (4) residual information of datadriven modeling and data mining analytics [18,19]. For example, Choi and Yoon [19] proposed an in situ backup virtual sensing system using an autoencoder for FDD in district heating substations.…”

Section: Introductionmentioning

confidence: 99%

Virtual In Situ Calibration for Operational Backup Virtual Sensors in Building Energy Systems

2022

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Intelligent building systems require a data-rich environment. Virtual sensors can provide informative and reliable sensing environments for operational datasets in building systems. In particular, backup virtual sensors that are in situ are beneficial for developing the counterparts of target physical sensors in the field, thus providing additional information about residuals between both types of sensors for use in data-driven modeling, analytics, and diagnostics. Therefore, to obtain virtual sensor potentials continuously during operation, we proposed an in situ calibration method for in situ backup virtual sensors (IBVS) in operational building energy systems, based on virtual in situ calibration (VIC). The proposed method was applied using operational datasets measured by a building automation system built into a target system. In a case study, the in situ virtual sensor showed large errors (the root mean squared error (RMSE) was 0.97 °C) on certain days. After conducting the proposed VIC, the RMSE of virtual sensor errors decreased by 22.7% and 18.7% from the perspective of sensor error types such as bias and random error, respectively, in the validation month. The subsequent virtual measurements could be considerably and effectively improved without retraining the specific in situ backup virtual sensor.

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Virtual sensor-assisted in situ sensor calibration in operational HVAC systems

Cited by 38 publications

References 34 publications

Control de caudal con un sensor virtual basado en técnicas de deep learning

Control de caudal con un sensor virtual basado en técnicas de deep learning

Virtual Sensors for Estimating District Heating Energy Consumption under Sensor Absences in a Residential Building

Virtual In Situ Calibration for Operational Backup Virtual Sensors in Building Energy Systems

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