Machine Learning for Accurate Office Room Occupancy Detection Using Multi-Sensor Data
Author:
Affiliation:
1. Department of Computer Engineering, Ahmadu Bello University, Zaria 810211, Nigeria
2. Department of Electrical and Electronics Engineering, Kaduna Polytechnic, Kaduna 800282, Nigeria
Publisher
MDPI
Link
https://www.mdpi.com/2673-4591/58/1/67/pdf
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2. Kim, Y.-M., Lee, Y.-H., and Pyo, C.-S. (2020, January 21–23). Accurate Occupancy Detection via Label Noise Filtering Technique. Proceedings of the 2020 International Conference on Information and Communication Technology Convergence (ICTC), Jeju Island, Republic of Korea.
3. Energy-efficient control of under-actuated HVAC zones in commercial buildings;Brooks;Energy Build.,2015
4. Zemouri, S., Gkoufas, Y., and Murphy, J. (2019, January 22–24). A machine learning approach to indoor occupancy detection using non-intrusive environmental sensor data. Proceedings of the Proceedings of the 3rd International Conference on Big Data and Internet of Things, Melbourn, Australia.
5. Thermal-sensor-based occupancy detection for smart buildings using machine-learning methods;Zhao;ACM Trans. Des. Autom. Electron. Syst. (TODAES),2018
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