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A simple model for automatic analysis and diagnosis of environmental thermal comfort in energy efficient buildings

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  • Balvís, Eduardo
  • Sampedro, Óscar
  • Zaragoza, Sonia
  • Paredes, Angel
  • Michinel, Humberto

Abstract

We present a mathematical model to diagnose HVAC systems in buildings based upon the analysis of a small number of ambient state variables. In particular, the equations of the model accurately fit recorded data of temperature, relative humidity and carbon dioxide concentration in different workplaces. For validation, data were obtained under different conditions and with different sensors. In particular, we designed and fabricated a wireless sensor that measures and transmits data to a remote device and we also applied our model to data collected using a commercial sensor. For each case, information was obtained that could be used to understand and predict the evolution of ambient variables that impact thermal comfort and energy consumption in buildings. The tools presented here can thus be of great interest to achieve affordable, smart energy-efficient buildings, while adhering to environmental laws and comfort for work spaces.

Suggested Citation

  • Balvís, Eduardo & Sampedro, Óscar & Zaragoza, Sonia & Paredes, Angel & Michinel, Humberto, 2016. "A simple model for automatic analysis and diagnosis of environmental thermal comfort in energy efficient buildings," Applied Energy, Elsevier, vol. 177(C), pages 60-70.
    • Handle: RePEc:eee:appene:v:177:y:2016:i:c:p:60-70
    DOI: 10.1016/j.apenergy.2016.04.117
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    Cited by:

    1. Roberto Robledo-Fava & Mónica C. Hernández-Luna & Pedro Fernández-de-Córdoba & Humberto Michinel & Sonia Zaragoza & A Castillo-Guzman & Romeo Selvas-Aguilar, 2019. "Analysis of the Influence Subjective Human Parameters in the Calculation of Thermal Comfort and Energy Consumption of Buildings," Energies, MDPI, vol. 12(8), pages 1-23, April.
    2. Barbeito, Inés & Zaragoza, Sonia & Tarrío-Saavedra, Javier & Naya, Salvador, 2017. "Assessing thermal comfort and energy efficiency in buildings by statistical quality control for autocorrelated data," Applied Energy, Elsevier, vol. 190(C), pages 1-17.
    3. Ogunjuyigbe, A.S.O. & Ayodele, T.R. & Akinola, O.A., 2017. "User satisfaction-induced demand side load management in residential buildings with user budget constraint," Applied Energy, Elsevier, vol. 187(C), pages 352-366.
    4. Schmidt, Mischa & Åhlund, Christer, 2018. "Smart buildings as Cyber-Physical Systems: Data-driven predictive control strategies for energy efficiency," Renewable and Sustainable Energy Reviews, Elsevier, vol. 90(C), pages 742-756.
    5. Li, Guannan & Hu, Yunpeng & Chen, Huanxin & Li, Haorong & Hu, Min & Guo, Yabin & Liu, Jiangyan & Sun, Shaobo & Sun, Miao, 2017. "Data partitioning and association mining for identifying VRF energy consumption patterns under various part loads and refrigerant charge conditions," Applied Energy, Elsevier, vol. 185(P1), pages 846-861.

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