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
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)
Figure 3.
Panoramic view of lecture hall where measurements were performed by thermal imaging
Figure 4.
Lecture hall layout. BMS – Building Management System, AC – Air-conditioning
Figure 5.
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
Figure 7.
Scatter plot for the steady-state scenario
Table 1.
Optimisation procedure results. One sensor was located where the BMS sensor is as a reference. Here “error” refers to the absolute error
| Scenario | Max Error (°C) | Min Error (°C) | MAPE (%) | |||
|---|---|---|---|---|---|---|
| Number of sensors | 1 | 3 | 1 | 3 | 1 | 3 |
| 1. Steady-state | 0.8 | 0.64 | 0 | 0 | 1.28 | 0.64 |
| 2. Intensive heating | 5.95 | 3.39 | 1.12 | 0.01 | 14.07 | 6.22 |
| 3. Intensive natural ventilation | 2.4 | 1.59 | 0 | 0.02 | 3.95 | 2.39 |
| 4. Air-conditioning | 6.1 | 6.00 | 0 | 0.02 | 13.52 | 13.24 |
Figure 8.
Scatter plot for the intensive heating scenario
Figure 9.
Scatter plot for the natural ventilation scenario
Figure 10.
Scatter plot for the air-conditioning scenario