Towards occupant activity driven smart buildings via WiFi-enabled IoT devices and deep learning
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Occupancy detection represents an opportunity for ML algorithms, ranging from decision trees to deep neural networks using various inputs.
“Moreover, occupancy detection itself represents an opportunity for ML algorithms, ranging from decision trees [193,882] to deep neural networks [896] that take input from occupancy sensors [193], WiFi signals [896,897], or appliance power consumption data [882].”
From Tackling climate change with machine learning (2023) · cites this paper as [896]
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Self-citation
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Cited by
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Tackling climate change with machine learning
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Self-citation
- Authors
- Zou, H., Zhou, Y., Yang, J., & Spanos, C. J.
- Affiliations
- University of California, Berkeley (US)
- Nanyang Technological University (SG)
- Corresponding author
- Zhou, Yuxun
- Venue
- Energy and Buildings
- Publisher
- Elsevier BV
- Published
- 2018-10
- Volume
- 177
- Pages
- 12-22
- ISSN
- 0378-7788 (ISSN-L)
- OpenAlex
- W2885580664
- Cited by
- 100
- References
- 60
- Citations by year
- 2025: 8 · 2024: 10 · 2023: 9 · 2022: 17 · 2021: 19 · 2020: 26 · 2019: 11
- FWCI
- 7.01
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Energy and Buildings (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Indoor and Outdoor Localization Technologies
- Social Sciences › Transportation › Human Mobility and Location-Based Analysis
- Keywords
- human activity recognition, smart buildings, WiFi channel state information, Internet of Things, long-term recurrent convolutional network, autoencoder, indoor positioning, energy efficiency, occupant behavior
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