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 30 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. 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

2. 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

3. Energy modelling and control of building heating and cooling systems with data-driven and hybrid models—A review;Renewable and Sustainable Energy Reviews;2023-09

4. Smart Spaces: Occupancy Detection using Adaptive Back-Propagation Neural Network;2023 International Conference on Business Analytics for Technology and Security (ICBATS);2023-03-07

5. Application of Machine Learning in Occupant and Indoor Environment Behavior Modeling: Sensors, Methods, and Algorithms;Handbook of Smart Energy Systems;2023

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