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Virtual models of indoor-air-quality sensors

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  • Kusiak, Andrew
  • Li, Mingyang
  • Zheng, Haiyang

Abstract

A data-driven approach for modeling indoor-air-quality (IAQ) sensors used in heating, ventilation, and air conditioning (HVAC) systems is presented. The IAQ sensors considered in the paper measure three basic parameters, temperature, CO2, and relative humidity. Three models predicting values of IAQ parameters are built with various data mining algorithms. Four data mining algorithms have been tested on the HVAC data set collected at an office-type facility. The computational results produced by models built with different data mining algorithms are discussed. The neural network (NN) with multi-layer perceptron (MLP) algorithms produced the best results for all three IAQ sensors among all algorithms tested. The models built with data mining algorithms can serve as virtual IAQ sensors in buildings and be used for on-line monitoring and calibration of the IAQ sensors. The approach presented in this paper can be applied to HVAC systems in buildings beyond the type considered in this paper.

Suggested Citation

  • Kusiak, Andrew & Li, Mingyang & Zheng, Haiyang, 2010. "Virtual models of indoor-air-quality sensors," Applied Energy, Elsevier, vol. 87(6), pages 2087-2094, June.
    • Handle: RePEc:eee:appene:v:87:y:2010:i:6:p:2087-2094
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    References listed on IDEAS

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    1. Kusiak, Andrew & Zheng, Haiyang & Song, Zhe, 2009. "Models for monitoring wind farm power," Renewable Energy, Elsevier, vol. 34(3), pages 583-590.
    2. Liang, Xia & Chan, M.Y. & Shiming, Deng, 2008. "Development of a method for calculating steady-state equipment sensible heat ratio of direct expansion air conditioning units," Applied Energy, Elsevier, vol. 85(12), pages 1198-1207, December.
    3. Mui, K.W. & Wong, L.T. & Law, L.Y., 2007. "An energy benchmarking model for ventilation systems of air-conditioned offices in subtropical climates," Applied Energy, Elsevier, vol. 84(1), pages 89-98, January.
    4. Wong, L.T. & Mui, K.W., 2008. "A transient ventilation demand model for air-conditioned offices," Applied Energy, Elsevier, vol. 85(7), pages 545-554, July.
    5. Du, Zhimin & Jin, Xinqiao & Yang, Yunyu, 2009. "Fault diagnosis for temperature, flow rate and pressure sensors in VAV systems using wavelet neural network," Applied Energy, Elsevier, vol. 86(9), pages 1624-1631, September.
    6. Aktacir, Mehmet Azmi & Büyükalaca, Orhan & YIlmaz, Tuncay, 2010. "A case study for influence of building thermal insulation on cooling load and air-conditioning system in the hot and humid regions," Applied Energy, Elsevier, vol. 87(2), pages 599-607, February.
    7. Kusiak, Andrew & Li, Mingyang, 2010. "Cooling output optimization of an air handling unit," Applied Energy, Elsevier, vol. 87(3), pages 901-909, March.
    8. Peeters, Leen & Dear, Richard de & Hensen, Jan & D'haeseleer, William, 2009. "Thermal comfort in residential buildings: Comfort values and scales for building energy simulation," Applied Energy, Elsevier, vol. 86(5), pages 772-780, May.
    9. Krüger, E.L. & Laroca, C., 2010. "Thermal performance evaluation of a low-cost housing prototype made with plywood panels in Southern Brazil," Applied Energy, Elsevier, vol. 87(2), pages 661-672, February.
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    Cited by:

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    2. Le Cam, M. & Daoud, A. & Zmeureanu, R., 2016. "Forecasting electric demand of supply fan using data mining techniques," Energy, Elsevier, vol. 101(C), pages 541-557.
    3. David Pejčoch, 2014. "CADAQUES: The Methodology for Complex Data and Information Management [CADAQUES: Metodika pro komplexní řízení kvality dat a informací]," Acta Informatica Pragensia, Prague University of Economics and Business, vol. 2014(1), pages 44-56.
    4. Shaikh, Faisal Karim & Zeadally, Sherali, 2016. "Energy harvesting in wireless sensor networks: A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 55(C), pages 1041-1054.
    5. Enescu, Diana, 2017. "A review of thermal comfort models and indicators for indoor environments," Renewable and Sustainable Energy Reviews, Elsevier, vol. 79(C), pages 1353-1379.
    6. Wei, Xiupeng & Xu, Guanglin & Kusiak, Andrew, 2014. "Modeling and optimization of a chiller plant," Energy, Elsevier, vol. 73(C), pages 898-907.
    7. Zhao, Yang & Wang, Shengwei & Xiao, Fu, 2013. "Pattern recognition-based chillers fault detection method using Support Vector Data Description (SVDD)," Applied Energy, Elsevier, vol. 112(C), pages 1041-1048.
    8. Andrew Kusiak & Xiupeng Wei, 2014. "Prediction of methane production in wastewater treatment facility: a data-mining approach," Annals of Operations Research, Springer, vol. 216(1), pages 71-81, May.
    9. Delin Wang & Xiangshun Li, 2022. "A Novel Virtual Sensor Modeling Method Based on Deep Learning and Its Application in Heating, Ventilation, and Air-Conditioning System," Energies, MDPI, vol. 15(15), pages 1-18, August.

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