AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
Article Link
Collect
Submit Manuscript
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Research Article

Impacts of HVACR temperature sensor offsets on building energy performance and occupant thermal comfort

Sungmin Yoon1Yuebin Yu2( )Jiaqiang Wang3Peng Wang2,4
Division of Architecture and Urban Design, Incheon National University, Incheon 22012, R.O. Korea
Durham School of Architectural Engineering and Construction, University of Nebraska-Lincoln, Lincoln, NE 68588, USA
College of Civil Engineering, Hunan University, Changsha, Hunan 410082, China
School of Civil Engineering, Dalian University of Technology, Dalian, Liaoning 116024, China
Show Author Information

Abstract

Many advanced systems and data analysis methods are introduced into building science to realize the building automation and smart buildings. They are highly dependent on the information and data obtained from building sensor networks. In this technical flow, it is considerably important to understand the impacts of sensor errors on building energy systems, including Heating, Ventilation, Air-conditioning, and Refrigeration (HVACR), and mechanisms behind that in developing the reliable sensing environments and applying sensing technologies. Especially, temperature sensor errors have the great impacts on the system control and the application performance connected with the building systems; However, it is more challenging to calibrate the erroneous temperature sensors using a recent novel sensor calibration method (virtual in-situ sensor calibration). Nevertheless, few studies have concentrated on the impacts of temperature sensor errors through HVACR systems and they still lack the quantitative results and the understanding of how the temperature errors affect building energy performance and thermal comfort in the previous studies. Thus, this study investigates and characterizes the various impacts of temperature errors in HVACR using building energy simulation with the individual and combined error cases. The analysis includes the changes in the energy consumptions, system operation, system performance, and occupant unmet set-point hours by error location and error size.

References

 
N Aste, M Manfren, G Marenzi (2017). Building automation and control systems and performance optimization: A framework for analysis. Renewable and Sustainable Energy Reviews, 75: 313–330.
 
M Basarkar, X Pang, L Wang, P Haves, T Hong (2011). Modeling and simulation of HVAC faults in EnergyPlus. In: Proceedings of IBPSA Building Simulation International Conference, Sydney, Australia.
 
H Cheung, JE Braun (2015). Development of fault models for hybrid fault detection and diagnostics algorithm. NREL/SR-5500-65030. Golden, CO, USA: National Renewable Energy Laboratory.
 
W Cho, D Song, S Hwang, S Yun (2015). Energy-efficient ventilation with air-cleaning mode and demand control in a multi-residential building. Energy and Buildings, 90: 6–14.
 
DOE (2016). EnergyPlus Documentation Engineering Reference Version 8.7. Washington, DC: U.S. Department of Energy.
 
Z Du, X Jin, Y Yang (2009). Fault diagnosis for temperature, flow rate and pressure sensors in VAV systems using wavelet neural network. Applied Energy, 86: 1624–1631.
 
C Fan, F Xiao, H Madsen, D Wang (2015). Temporal knowledge discovery in big BAS data for building energy management. Energy and Buildings, 109: 75–89.
 
DC Gao, S Wang, K Shan, C Yan (2016). A system-level fault detection and diagnosis method for low delta-T syndrome in the complex HVAC systems. Applied Energy, 164: 1028–1038.
 
H Grindvoll, O Vermesan, T Crosbie, R Bahr, N Dawood, GM Revel (2012). A wireless sensor network for intelligent building energy management based on multi communication standards—A case study. Journal of Information Technology in Construction, 17: 43–62.
 
WS Jang, WM Healy (2010). Wireless sensor network performance metrics for building applications. Energy and Buildings, 42: 862– 868.
 
JY Kao, E Pierce (1983). Sensor errors and their effect on building energy consumption. ASHRAE Journal, 25(12): 42–45.
 
H Li, D Yu, JE Braun (2011). A review of virtual sensing technology and application in building systems. HVAC&R Research, 17: 619–645.
 
G Li, Y Hu, H Chen, H Li, M Hu, Y Guo, S Shi, W Hu (2016a). A sensor fault detection and diagnosis strategy for screw chiller system using support vector data description-based D-statistic and DV-contribution plots. Energy and Buildings, 133: 230–245.
 
HX Li, M Gül, H Yu, HAM Al-hussein (2016b). An energy performance monitoring, analysis and modelling framework for NetZero Energy Homes (NZEHs). Energy and Buildings, 126: 353–364.
 
H Lim, ZJ Zhai (2017a). Comprehensive evaluation of the influence of meta-models on Bayesian calibration. Energy and Buildings, 155: 66–75.
 
H Lim, ZJ Zhai (2017b). Review on stochastic modeling methods for building stock energy prediction. Building Simulation, 10: 607–624.
 
J Ma, JCP Cheng (2016). Estimation of the building energy use intensity in the urban scale by integrating GIS and big data technology. Applied Energy, 183: 182–192.
 
K Roth, D Westphalen, P Llana, M Feng (2004). The energy impact of faults in U.S. commercial buildings. In: Proceedings of the International Refrigeration and Air Conditioning Conference, West Lafayette, IN, USA.
 
Texas Instruments (2017). User guides for sensing products. Available at http://www.ti.com/lsds/ti/sensing-products/temperature-sensors/ temperature-sensors-overview.page.
 
J Verhelst, GV Ham, D Saelens, L Helsen (2017). Economic impact of persistent sensor and actuator faults in concrete core activated office buildings. Energy and Buildings, 142: 111–127.
 
Z Wang, Z Wang, S He, X Gu, ZF Yan (2017). Fault detection and diagnosis of chillers using Bayesian network merged distance rejection and multi-source non-sensor information. Applied Energy, 188: 200–214.
 
J Wang, Q Zhang, Y Yu, X Chen, S Yoon (2018). Application of model-based control strategy to hybrid free cooling system with latent heat thermal energy storage for TBSs. Energy and Buildings, 167: 89–105.
 
T Yang, Y Pan, J Mao, Y Wang, Z Huang (2016). An automated optimization method for calibrating building energy simulation models with measured data: Orientation and a case study. Applied Energy, 179: 1220–1231.
 
S Yoon, J Seo, W Cho, D Song (2015). A calibration method for whole-building airflow simulation in high-rise residential buildings. Building and Environment, 85: 253–262.
 
S Yoon, Y Yu (2016). Autonomous in-situ sensor calibration in building systems using Bayesian inference. In: Proceedings of the 9th IAQVEC Conference, Songdo, R.O. Korea.
 
S Yoon, Y Yu (2017a). A quantitative comparison of statistical and deterministic methods on virtual in-situ calibration in building systems. Building and Environment, 115: 54–66.
 
S Yoon, Y Yu (2017b). Comparison of stochastic and deterministic optimization algorithms on virtual in-situ calibration in building systems. In: Proceedings of 2017 ASHRAE Winter Conference, Las Vegas, NV, USA.
 
S Yoon, Y Yu (2017c). Extended virtual in-situ calibration method in building systems using Bayesian inference. Automation in Construction, 73: 20–30.
 
S Yoon, Y Yu (2018a). A sensitivity effect on virtual in-situ sensor calibration in building energy systems. In: Proceedings of 2018 ASHRAE Winter Conference, Chicago, IL, USA.
 
S Yoon, Y Yu (2018b). Hidden factors and handling strategy for accuracy of virtual in-situ sensor calibration in building energy systems: Sensitivity effect and reviving calibration. Energy and Buildings, 170: 217–228.
 
S Yoon, Y Yu (2018c). Hidden factors and handling strategies on virtual in-situ sensor calibration in building energy systems: Prior information and cancellation effect. Applied Energy, 212: 1069–1082.
 
S Yoon, Y Yu (2018d). Strategies for virtual in-situ sensor calibration in building energy systems. Energy and Buildings, 172: 22–34.
 
D Yu, H Li, Y Yu, J Xiong (2011). Virtual calibration of a supply air temperature sensor in rooftop air conditioning units. HVAC&R Research, 17: 31–50.
 
Y Yu, D Woradechjumroen, D Yu (2014). A review of fault detection and diagnosis methodologies on air-handling units. Energy and Buildings, 82: 550–562.
 
Y Yu, H Li (2015). Virtual in-situ calibration method in building systems. Automation in Construction, 59: 59–67.
 
R Zhang, T Hong (2017). Modeling of HVAC operational faults in building performance simulation. Applied Energy, 202: 178–188.
Building Simulation
Pages 259-271
Cite this article:
Yoon S, Yu Y, Wang J, et al. Impacts of HVACR temperature sensor offsets on building energy performance and occupant thermal comfort. Building Simulation, 2019, 12(2): 259-271. https://doi.org/10.1007/s12273-018-0475-3

601

Views

45

Crossref

N/A

Web of Science

45

Scopus

0

CSCD

Altmetrics

Received: 28 December 2017
Revised: 29 August 2018
Accepted: 09 September 2018
Published: 11 October 2018
© Tsinghua University Press and Springer-Verlag GmbH Germany, part of Springer Nature 2018
Return