Abstract
In this paper, we implement a convolutional neural networks based autoencoder to investigate occupancy profiles. We use the American time use survey data, which contains 191,558 schedules with binary occupancy information. Our results suggest that the trained filters provide an important insight of occupancy profiles (i.e., dominant and distinct patterns), and the latent space compresses the profiles with representative information.
Original language | American English |
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Number of pages | 5 |
State | Published - 2020 |
Event | 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation (BuildSys 2019) - New York, New York Duration: 13 Nov 2019 → 14 Nov 2019 |
Conference
Conference | 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation (BuildSys 2019) |
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City | New York, New York |
Period | 13/11/19 → 14/11/19 |
NREL Publication Number
- NREL/CP-5500-74892
Keywords
- autoencoder
- convolutional neural networks
- occupant behavior