A virtual supply airflow rate meter for rooftop air-conditioning units

Yu, Daihong; Li, Haorong; Yang, Mo

doi:10.1016/j.buildenv.2010.12.017

Cited by 53 publications

(12 citation statements)

References 73 publications

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“…Yu et al [20] developed a steady-state gray-box based virtual meter of the supply airflow rate in packaged rooftop units from the measurements in the laboratory settings. The nonlinear correlation models use as regressors the supply and outdoor air temperatures, and outside air damper status.…”

Section: Machine Learning (Ml) For Benchmarking Modelsmentioning

confidence: 99%

Machine Learning for Benchmarking Models of Heating Energy Demand of Houses in Northern Canada

Bezyan

Zmeureanu

2020

Energies

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In most cases, the benchmarking models of energy use in houses are developed based on current and past data, and they continue to be used without any update. This paper proposes the method of retraining of benchmarking models by applying machine learning techniques when new measurements are made available. The method uses as a case study the measurements of heating energy demand from two semi-detached houses of Northern Canada. The results of the prediction of heating energy demand using static or augmented window techniques are compared with measurements. The daily energy signature is used as a benchmarking model due to its simplicity and performance. However, the proposed retraining method can be applied to any form of benchmarking model. The method should be applied in all possible situations, and be an integral part of intelligent building automation and control systems (BACS) for the ongoing commissioning for building energy-related applications.

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Section: Machine Learning (Ml) For Benchmarking Modelsmentioning

confidence: 99%

Machine Learning for Benchmarking Models of Heating Energy Demand of Houses in Northern Canada

Bezyan

Zmeureanu

2020

Energies

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“…Kusiak et al (2010) used neural network models to estimate temperature, relative humidity and carbon dioxide concentration in an indoor environment based on outdoor conditions. Yu et al (2011) presented a virtual sensor for airflows in packaged air conditioners. Zhao et al (2012) developed a condenser fouling sensor for water-cooled chillers.…”

Section: Introductionmentioning

confidence: 99%

Minimizing data collection for field calibration of steady-state virtual sensors for HVAC equipment

Cheung

Braun

2016

International Journal of Refrigeration

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Highlights Algorithm to minimize field data collection to calibrate HVAC virtual sensors  Criteria to end data collection based on sensor accuracy, reliability and applicability  Quantification of the accuracy, reliability and applicability of virtual sensors AbstractThe paper presents an algorithm to minimize the amount of data collection for calibration of steady-state virtual sensors when obtained during normal operation of HVAC equipment in the field. If virtual sensors were calibrated using laboratory data, the amount of the calibration data could be minimized using design of experiment techniques. However, these techniques are not applicable to data from field operating equipment since most of the operating conditions are not controllable (e.g., ambient temperature, loads). In this paper, an algorithm to minimize the data collection period for calibration of steady-state virtual sensors is developed based on sensor accuracy, reliability and applicability. Application of the method is demonstrated for calibration of hourly electricity consumption virtual sensors for 3 packaged air conditioners that are serving a small commercial building, and the method terminated the calibration process after about 3 weeks of data collection for all units with very accurate estimates of electricity consumption.

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“…Yu et al (2011) proposed a virtual supply airflow rate meter for rooftop units based on measuring outdoor air, return air, and supply air temperatures and outdoor air and zone air wet bulb temperatures using empirical cooling capacity of the direct expansion (DX) coil. Zhao et al (2012Zhao et al ( , 2014 and Zhao (2015) estimated the chilled and condenser water mass flow rates through the evaporator and condenser of a chiller using the energy balance on the compressor, evaporator, and condenser.…”

Section: Introductionmentioning

confidence: 99%

Development of a virtual pump water flow meter using power derived from comprehensive energy loss analysis

Andiroglu

Wang

Song

et al. 2016

Science and Technology for the Built Environment

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Water flow rates are key controlled variables in chilled and hot water systems. The water flow rate through a pump can be virtually determined using a measured pump head and input power. Available input power can be either motor input power obtained from the connected variable-frequency drive or variable-frequency drive input power measured by a power meter. The harmonics created by the variable-frequency drive induce additional energy loss in the motor and consequently degrade the motor efficiency. The objective of this article is to evaluate a virtual pump water flow meter using either the variable-frequency drive input power or motor input power with calibrated pump, motor and variable-frequency drive efficiencies along with the consideration of additional harmonic energy loss. First, the water flow correlation with the measured pump head and available power input is discussed, then the calibration procedure is introduced to identify three efficiencies, and finally the experiment is conducted to develop and validate a virtual pump water flow meter on a pump motor variable-frequency drive system. The experimental results show that the water flow rates determined by the virtual water flow meter agrees well with the results measured by a physical water flow meter with the coefficient of determination of 0.91and 0.97, respectively.

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A virtual supply airflow rate meter for rooftop air-conditioning units

Cited by 53 publications

References 73 publications

Machine Learning for Benchmarking Models of Heating Energy Demand of Houses in Northern Canada

Machine Learning for Benchmarking Models of Heating Energy Demand of Houses in Northern Canada

Minimizing data collection for field calibration of steady-state virtual sensors for HVAC equipment

Development of a virtual pump water flow meter using power derived from comprehensive energy loss analysis

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