Smart City Gnosys

Smart city article details

Title	Machine Learning Based Occupancy Detection In Smart Environments: A Residential Flat Case
ID_Doc	35937
Authors	Ayrancı A.; Akgün G.
Year	2024
Published	2024 Innovations in Intelligent Systems and Applications Conference, ASYU 2024
DOI	http://dx.doi.org/10.1109/ASYU62119.2024.10757150
Abstract	With the increasing spread of smart cities and smart building technologies, the concept of the Internet of Things (IoT) has emerged, and their control has also gained importance. Automatic control of devices in apartments in a smart building is important in terms of energy saving and comfort. In this context, this study focuses on detecting the occupancy in order to automatically control the devices in a smart home. To achieve this, measurements of inorganic gas, temperature, pressure, humidity and light levels in the residential flat are used. An original dataset of approximately 5 days duration is collected with the developed sensor device, obtained data is processed with various machine learning methods and occupancy detection is carried out. © 2024 IEEE.
Author Keywords	energy saving; machine learning; occupancy detection; smart environment

Similar Articles

Id	Similarity	Authors	Title	Published
5161	0.883	Masroor M.; Rezazadeh J.; Ayoade J.; Aliehyaei M.	A Survey Of Intelligent Building Automation With Machine Learning And Iot	Advances in Building Energy Research, 17, 3 (2023)
35718	0.88	Liang X.; Shim J.; Anderton O.; Song D.	Low-Cost Data-Driven Estimation Of Indoor Occupancy Based On Carbon Dioxide (Co2) Concentration: A Multi-Scenario Case Study	Journal of Building Engineering, 82 (2024)
33687	0.874	Chidurala V.; Wang X.; Li X.; Hamner J.	Iot Based Sensor System Design For Real-Time Non-Intrusive Occupancy Monitoring	Proceedings of the 2022 IEEE International Conference on Internet of Things and Intelligence Systems, IoTaIS 2022 (2022)
11363	0.873	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)
51056	0.873	Saleem A.A.; Hassan M.M.; Ali I.A.	Smart Homes Powered By Machine Learning: A Review	Proceedings of the 2nd 2022 International Conference on Computer Science and Software Engineering, CSASE 2022 (2022)
36050	0.868	Samikwa E.; Schärer J.; Braun T.; Di Maio A.	Machine Learning-Based Energy Optimisation In Smart City Internet Of Things	Proceedings of the International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc) (2023)
30897	0.867	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)
8624	0.866	Peralta Abadía J.J.; Smarsly K.	An Introduction And Systematic Review On Machine Learning For Smart Environments/Cities: An Iot Approach	Intelligent Systems Reference Library, 121 (2022)
932	0.866	Lavrinovica I.; Judvaitis J.; Laksis D.; Skromule M.; Ozols K.	A Comprehensive Review Of Sensor-Based Smart Building Monitoring And Data Gathering Techniques	Applied Sciences (Switzerland), 14, 21 (2024)
24059	0.864	Sudhakar K.; Lurdhumary J.; Bathrinath S.; Howard E.; Vijayakumar G.N.S.; Anusuya M.; Robin C.R.R.	Enhancing Urban Iot Temperature Sensing Accuracy Through Machine Learning-Driven Dynamic Ventilation	AIP Conference Proceedings, 3193, 1 (2024)