Sensor Fusion for Occupancy Estimation: A Study Using Multiple Lecture Rooms in a Complex Building
Abstract
:1. Introduction
- How much can the occupancy estimation accuracy be improved using multiple sensors?
- How can one single model be trained for all rooms in a building?
- How does the quality of the estimation in a room behave when only using data from other rooms?
2. Materials and Methods
2.1. Methodology
2.2. Data Recording
2.3. Preprocessing
2.4. Machine Learning Approach
3. Results and Discussion
4. Conclusions and Future Work
- Wherever applicable, due to infrastructure, multiple sensors should be used for data gathering. The quality of estimation always benefits from combining different sensors, compared to models with only one sensor. However, using all sensors might not be the best solution. Through test cases, the best combination of different sensors should be determined. In the case of our study, we improved the RMSE from 17.4 to 6.2, combining different features compared to only using one feature.
- It is possible to train a single model for all rooms in a building. However, the model must be trained with data from all rooms in the building, which may lead to higher costs in bigger buildings with more rooms. This leads to our final finding.
- When defining training data for the model, the dataset should contain data from every room. A trained model from certain rooms shows no convincing results when tested in a new unknown room. This shows the complex differences in infrastructure inside a building. By only testing their model on one or two rooms, almost all studies did not respect this factor. For smaller buildings with fewer rooms, the effort would be manageable. For bigger buildings, sensors should be integrated into infrastructure and the data readings should be as automatic as possible to minimize effort.
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Date | Time | Identifier | Room Size [m2] | People in Exam |
---|---|---|---|---|
4 July 2022 | 09:47–12:32 | Room 1 | 407.75 | 49 |
6 July 2022 | 07:25–10:25 | Room 1 | 407.75 | 58 |
6 July 2022 | 10:13–12:58 | Room 2 | 59.61 | 6 |
7 July 2022 | 11:35–14:21 | Room 3 | 59.01 | 16 |
8 July 2022 | 08:25–12:30 | Room 4 | 90.23 | 15 |
8 July 2022 | 12:30–17:01 | Room 4 | 90.23 | 25 |
11 July 2022 | 08:33–11:24 | Room 3 | 59.01 | 10 |
12 July 2022 | 08:28–11:44 | Room 5 | 78.40 | 12 |
12 July 2022 | 11:52–14:49 | Room 2 | 59.61 | 13 |
13 July 2022 | 08:40–11:50 | Room 5 | 78.40 | 22 |
14 July 2022 | 09:29–12:05 | Room 5 | 78.40 | 18 |
14 July 2022 | 12:05–14:19 | Room 5 | 78.40 | 8 |
15 July 2022 | 08:42–11:27 | Room 1 | 407.75 | 45 |
Attribute | Feature Importance | Standard Error | p-Value |
---|---|---|---|
Bluetooth | 10.82 | 0.336 | 0.000 |
Wi-Fi | 1.59 | 0.361 | 0.000 |
Carbon dioxide | −1.03 | 0.287 | 0.000 |
Temperature | −0.28 | 0.259 | 0.283 |
Relative humidity | 0.47 | 0.286 | 0.098 |
Test Room | Training | Testing | RMSE | CV [%] |
---|---|---|---|---|
Room 1 | 0.70 | −2.10 | 31.4 | 82.6 |
Room 2 | 0.68 | 0.67 | 2.7 | 38.6 |
Room 3 | 0.71 | −1.82 | 8.5 | 77.3 |
Room 4 | 0.78 | −0.05 | 10.4 | 74.3 |
Room 5 | 0.85 | −2.81 | 13.6 | 123.6 |
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Roussel, C.; Böhm, K.; Neis, P. Sensor Fusion for Occupancy Estimation: A Study Using Multiple Lecture Rooms in a Complex Building. Mach. Learn. Knowl. Extr. 2022, 4, 803-813. https://doi.org/10.3390/make4030039
Roussel C, Böhm K, Neis P. Sensor Fusion for Occupancy Estimation: A Study Using Multiple Lecture Rooms in a Complex Building. Machine Learning and Knowledge Extraction. 2022; 4(3):803-813. https://doi.org/10.3390/make4030039
Chicago/Turabian StyleRoussel, Cédric, Klaus Böhm, and Pascal Neis. 2022. "Sensor Fusion for Occupancy Estimation: A Study Using Multiple Lecture Rooms in a Complex Building" Machine Learning and Knowledge Extraction 4, no. 3: 803-813. https://doi.org/10.3390/make4030039