TY - JOUR
KW - Data fusion
KW - Machine learning
KW - Occupancy prediction
KW - Physics-based model
KW - Feature selection
AU - Wei Wang
AU - Tianzhen Hong
AU - Ning Xu
AU - Xiaodong Xu
AU - Jiayu Chen
AU - Xiaofang Shan
AB - <p><span>Fusing various sensing data sources can significantly improve the accuracy and reliability of building occupancy detection. Fusing environmental sensors and wireless network signals are seldom studied for its computational and technical complexity. This study aims to propose an integrated adaptive lasso model that is able to extract critical data features for environmental and Wi-Fi probe dual sensing sources. Through rapid feature extraction and process simplification, the proposed method aims to improve the computational efficiency of occupancy detecting models. To validate the proposed model, an onsite experiment was conducted to examine two occupancy data resolutions, (real-time and four-level occupancy resolutions). The results suggested that, among all twelve features, eight features are most relevant. The mean absolute error of the real-time occupancy can be reduced to 2.18 and F1_accuracy is about 84.36% for the four-level occupancy.</span></p>
BT - Building and Environment
DA - 01/2019
DO - 10.1016/j.buildenv.2019.106280
LA - eng
N2 - <p><span>Fusing various sensing data sources can significantly improve the accuracy and reliability of building occupancy detection. Fusing environmental sensors and wireless network signals are seldom studied for its computational and technical complexity. This study aims to propose an integrated adaptive lasso model that is able to extract critical data features for environmental and Wi-Fi probe dual sensing sources. Through rapid feature extraction and process simplification, the proposed method aims to improve the computational efficiency of occupancy detecting models. To validate the proposed model, an onsite experiment was conducted to examine two occupancy data resolutions, (real-time and four-level occupancy resolutions). The results suggested that, among all twelve features, eight features are most relevant. The mean absolute error of the real-time occupancy can be reduced to 2.18 and F1_accuracy is about 84.36% for the four-level occupancy.</span></p>
PY - 2019
EP - 106280
ST - Building and Environment
T2 - Building and Environment
TI - Cross-source sensing data fusion for building occupancy prediction with adaptive lasso feature filtering
VL - 162
SN - 03601323
ER -