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Positioning Temperature Sensors in Confined Spaces Subject to Various Exogenous Impacts

Architecture, Civil Engineering, Environment
Volume 11 (2018): Issue 1 (March 2018)
By:
Open Access
|Apr 2019

Figures & Tables

Figure 1.

Daily observed mean, maximal and minimal temperatures in Kraków over 2016 [5], with relation to the temperature range suggested by [6] for an office building at 60% humidity
Daily observed mean, maximal and minimal temperatures in Kraków over 2016 [5], with relation to the temperature range suggested by [6] for an office building at 60% humidity

Figure 2.

Schematic overview for estimating the temperature (t1’=?) at a point marked by the blue square by means of temperatures measured at three points (yellow circles). Visualisation on plane coordinates (XY)
Schematic overview for estimating the temperature (t1’=?) at a point marked by the blue square by means of temperatures measured at three points (yellow circles). Visualisation on plane coordinates (XY)

Figure 3.

Panoramic view of lecture hall where measurements were performed by thermal imaging
Panoramic view of lecture hall where measurements were performed by thermal imaging

Figure 4.

Lecture hall layout. BMS – Building Management System, AC – Air-conditioning
Lecture hall layout. BMS – Building Management System, AC – Air-conditioning

Figure 5.

Maps visualising temperature (°C) distribution in investigated room under four scenarios
Maps visualising temperature (°C) distribution in investigated room under four scenarios

Figure 6.

Optimal locations for three sensors to estimate spatial temperature distribution with the highest precision for the four scenarios
Optimal locations for three sensors to estimate spatial temperature distribution with the highest precision for the four scenarios

Figure 7.

Scatter plot for the steady-state scenario
Scatter plot for the steady-state scenario

Figure 8.

Scatter plot for the intensive heating scenario
Scatter plot for the intensive heating scenario

Figure 9.

Scatter plot for the natural ventilation scenario
Scatter plot for the natural ventilation scenario

Figure 10.

Scatter plot for the air-conditioning scenario
Scatter plot for the air-conditioning scenario

Optimisation procedure results_ One sensor was located where the BMS sensor is as a reference_ Here “error” refers to the absolute error | t j ′ - t j |

ScenarioMax Error (°C)Min Error (°C)MAPE (%)
Number of sensors131313
1. Steady-state0.80.64001.280.64
2. Intensive heating5.953.391.120.0114.076.22
3. Intensive natural ventilation2.41.5900.023.952.39
4. Air-conditioning6.16.0000.0213.5213.24
DOI: https://doi.org/10.21307/acee-2018-001 | Journal eISSN: 2720-6947 | Journal ISSN: 1899-0142
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Language: English
Page range: 5 - 14
Submitted on: Sep 8, 2017
Accepted on: Mar 3, 2018
Published on: Apr 1, 2019
Published by: Silesian University of Technology
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
Keywords:
Temperature spatial distribution,
Lecturing hall,
Thermal comfort,
Optimisation
Related subjects:
Architecture and design,
Architecture,
Architects, buildings

© 2019 Grzegorz AUGUSTYN, Jakub JURASZ, Krzysztof JURCZYK, Tomasz KORBIEL, Rafał RUMIN, Jerzy MIKULIK, published by Silesian University of Technology
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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