Smart City Gnosys
Smart city article details
| Title | Low-Cost Data-Driven Estimation Of Indoor Occupancy Based On Carbon Dioxide (Co2) Concentration: A Multi-Scenario Case Study |
|---|---|
| ID_Doc | 35718 |
| Authors | Liang X.; Shim J.; Anderton O.; Song D. |
| Year | 2024 |
| Published | Journal of Building Engineering, 82 |
| DOI | http://dx.doi.org/10.1016/j.jobe.2023.108180 |
| Abstract | Occupancy levels significantly influence HVAC system operation, making accurate occupancy prediction essential for the advancement of Occupant-Centered HVAC control. This study aims to develop a simple and effective occupant prediction model in buildings using low-cost indoor environmental sensors and artificial intelligence technology. In-situ measurements were taken in two university classrooms in South Korea over a three-month period, collecting data on indoor and outdoor temperature, humidity, and CO2 levels. Five machine learning algorithms, including Linear Regression (LR), Random Forest (RF), Gradient Boosting Regression (GBR), Multi-Layer Perceptron (MLP), and Long Short-Term Memory neural networks (LSTM), were applied to compare models of indoor occupancy. The results demonstrate that, among the five machine learning models evaluated, the LSTM model outperforms the others, achieving an RMSE of 3.43. This result indicates a close match between predicted and actual indoor occupancy based on CO2 concentration. The integration of a multivariate multi-step input method further enhances its accuracy, making it suitable for a variety of real-world scenarios in indoor occupancy prediction. This study reveals that using processed data as input sources leads to improved prediction performance for indoor occupant states. Importantly, this work does not infringe on biometric information, such as human image privacy, and relies on minimal measurement data. Furthermore, it not only emphasizes the model's feasibility and practicality in predicting indoor occupancy but also its potential in HVAC system automation, building energy conservation, and indoor environmental management. This study offers guidance and support for the advancement of smart cities and intelligent buildings in the future. © 2023 |
| Author Keywords | Indoor CO<sub>2</sub> concentration; LSTM; Machine learning; Occupancy |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
23223  | 0.903 | Abdel-Razek S.A.; Marie H.S.; Alshehri A.; Elzeki O.M. | Energy Efficiency Through The Implementation Of An Ai Model To Predict Room Occupancy Based On Thermal Comfort Parameters | Sustainability (Switzerland), 14, 13 (2022) |
| 11363 | 0.89 | Dinarvand P.; Han L.; Coates A.; Han L. | Automatic Real-Time Prediction Of Energy Consumption Based On Occupancy Pattern For Energy Efficiency Management In Buildings | Proceedings - 20th International Conference on High Performance Computing and Communications, 16th International Conference on Smart City and 4th International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2018 (2019) |
| 30897 | 0.883 | Samareh Abolhassani S.; Zandifar A.; Ghourchian N.; Amayri M.; Bouguila N.; Eicker U. | Improving Residential Building Energy Simulations Through Occupancy Data Derived From Commercial Off-The-Shelf Wi-Fi Sensing Technology | Energy and Buildings, 272 (2022) |
| 35937 | 0.88 | Ayrancı A.; Akgün G. | Machine Learning Based Occupancy Detection In Smart Environments: A Residential Flat Case | 2024 Innovations in Intelligent Systems and Applications Conference, ASYU 2024 (2024) |
| 57886 | 0.868 | Batal M.; Daissaoui A.; Filali E.L.S.; Lbath A. | Towards A Novel Approach Based On Machine Learning To Assess Environmental Suitability In Smart Campuses | Procedia Computer Science, 236 (2024) |
| 48370 | 0.868 | Chu Y.; Cetin K. | Sensing Systems For Smart Building Occupant-Centric Operation | The Rise of Smart Cities: Advanced Structural Sensing and Monitoring Systems (2022) |
| 42791 | 0.868 | Zou Y.; Xie W.; Lou S.; Huang Y.; Xia D.; Yang X.; Feng C. | Prediction Model Establishment For Residential Community Occupancy Considering Urban Environment | Journal of Building Engineering, 96 (2024) |
| 31255 | 0.859 | Khatouni A.S.; Bauer M.; Lutfiyya H. | Indoor Temperature Characterization And Its Implication On Power Consumption In A Campus Building | 2020 7th International Conference on Internet of Things: Systems, Management and Security, IOTSMS 2020 (2020) |
| 7452 | 0.858 | MacIa-Perez F.; Lorenzo-Fonseca I.; Berna-Martinez J.-V. | An Ai-Based Ventilation Kpi Using Embedded Iot Devices | IEEE Embedded Systems Letters, 16, 1 (2024) |
| 23280 | 0.856 | Huang J.; Algahtani M.; Kaewunruen S. | Energy Forecasting In A Public Building: A Benchmarking Analysis On Long Short-Term Memory (Lstm), Support Vector Regression (Svr), And Extreme Gradient Boosting (Xgboost) Networks | Applied Sciences (Switzerland), 12, 19 (2022) |