Enhancing real-time nonintrusive occupancy estimation in buildings via knowledge fusion network

Lu, C.J.

doi:10.1016/j.enbuild.2023.113812

Enhancing real-time nonintrusive occupancy estimation in buildings via knowledge fusion network

Title

Enhancing real-time nonintrusive occupancy estimation in buildings via knowledge fusion network

Author

Lu, C.J. (TU Delft Environmental & Climate Design; Guangdong University of Technology)

Date

2024

Abstract

Real-time nonintrusive occupancy estimation can maximize the use of existing sensors to infer occupant information in buildings with the advantages of fewer privacy concerns and fewer extra device costs. Recently, many deep learning architectures have proven effective in estimating occupancy directly from raw sensor data. However, some handcrafted features manually extracted from statistical and temporal domains might convey additional information for occupancy estimation. In this study, a novel knowledge fusion network for nonintrusive occupancy estimation is proposed to integrate knowledge from two streams, i.e. automatic knowledge stream from a deep learning architecture and handcrafted knowledge stream from manual feature engineering. Moreover, four different fusion modules are investigated to optimize the design of the fusion network. To verify the effectiveness of the proposed network, experiments are conducted in a dataset from the ASHRAE Global Occupant Behavior Database, which is collected from an office space with records of indoor environment parameters, occupant-building interactions, and contextual information. The results demonstrate the superiority of the proposed fusion network, which outperforms five representative algorithms. Furthermore, the ablation study underscores the benefits of knowledge fusion and occupant-building interaction information, showing that the proposed fusion network can enhance the occupancy estimation accuracy by 3.47 % to 9.24 %.

Subject

Deep learning
Network fusion
Nonintrusive sensing
Occupancy estimation
Smart buildings

To reference this document use:

http://resolver.tudelft.nl/uuid:50d362da-1302-45a9-9bde-ce8ae5e71677

DOI

https://doi.org/10.1016/j.enbuild.2023.113812

ISSN

0378-7788

Source

Energy and Buildings, 303

Part of collection

Institutional Repository