Estimation of building occupancy levels through environmental signals deconvolution

Author:

Ebadat Afrooz1,Bottegal Giulio1,Varagnolo Damiano1,Wahlberg Bo1,Johansson Karl H.1

Affiliation:

1. ACCESS Linnaeus Centre, School of Electrical Engineering, KTH Royal Institute of Technology

Publisher

ACM

Cited by 33 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Efficient data-driven occupancy detection in office environments and feature impact analysis;International Journal of Information Technology;2024-08-12

2. Occupancy Prediction in IoT-Enabled Smart Buildings: Technologies, Methods, and Future Directions;Sensors;2024-05-21

3. People, space use and objects: an UWB-based quantifying approach for post-occupancy evaluation of new architectural spaces;Engineering, Construction and Architectural Management;2024-03-26

4. OccuVAE: Integrating unsupervised occupancy inference in data-driven energy modeling for human-centric operation;Proceedings of the 10th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation;2023-11-15

5. Overcoming Data Scarcity through Transfer Learning in CO2-Based Building Occupancy Detection;Proceedings of the 10th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation;2023-11-15

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