Overcoming Data Scarcity through Transfer Learning in CO2-Based Building Occupancy Detection-Reference-Cited by-同舟云学术

Overcoming Data Scarcity through Transfer Learning in CO2-Based Building Occupancy Detection

Published:2023-11-15 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 10th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation
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Author:

Weber Manuel¹^ORCID,Banihashemi Farzan²^ORCID,Mandl Peter¹^ORCID,Jacobsen Hans-Arno³^ORCID,Mayer Ruben⁴^ORCID

Affiliation:

1. Munich University of Applied Sciences (HM), Germany

2. Technical University of Munich, Germany

3. University of Toronto, Canada

4. University of Bayreuth, Germany

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3600100.3623718

Reference41 articles.

1. Room-level occupant counts, airflow and CO 2 data from an office building

2. A Scalable Room Occupancy Prediction with Transferable Time Series Decomposition of CO 2 Sensor Data

3. DA-HOC

4. Farzan Banihashemi and Manuel Weber. 2023. "Non-intrusive indoor environmental data in two double-occupied offices". Mendeley Data (2023). https://doi.org/10.17632/v7kw9ycccp.1 10.17632/v7kw9ycccp.1

5. Farzan Banihashemi and Manuel Weber. 2023. "Non-intrusive indoor environmental data in two double-occupied offices". Mendeley Data (2023). https://doi.org/10.17632/v7kw9ycccp.1

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1. Coddora: CO2-Based Occupancy Detection Model Trained via Domain Randomization;2024 International Joint Conference on Neural Networks (IJCNN);2024-06-30

2. LLMSense: Harnessing LLMs for High-level Reasoning Over Spatiotemporal Sensor Traces;2024 IEEE 3rd Workshop on Machine Learning on Edge in Sensor Systems (SenSys-ML);2024-05-13

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