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A platform for research: civil engineering, architecture and urbanism
Cross-source sensing data fusion for building occupancy prediction with adaptive lasso feature filtering
https://orcid.org/0000-0003-1886-9137
https://orcid.org/0000-0001-9396-0059
Abstract 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.
Highlights Cross-source data fusion can improve occupancy detection accuracy. Environmental and Wi-Fi prob sensing can be integrated with feature sets. The adaptive lasso was implemented in feature selection. CO2 concentration, temperature, and Wi-Fi signal indicators are the most correlated features.
Cross-source sensing data fusion for building occupancy prediction with adaptive lasso feature filtering
https://orcid.org/0000-0003-1886-9137
https://orcid.org/0000-0001-9396-0059
Abstract 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.
Highlights Cross-source data fusion can improve occupancy detection accuracy. Environmental and Wi-Fi prob sensing can be integrated with feature sets. The adaptive lasso was implemented in feature selection. CO2 concentration, temperature, and Wi-Fi signal indicators are the most correlated features.
Cross-source sensing data fusion for building occupancy prediction with adaptive lasso feature filtering
https://orcid.org/0000-0003-1886-9137
https://orcid.org/0000-0001-9396-0059
Abstract 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.
Highlights Cross-source data fusion can improve occupancy detection accuracy. Environmental and Wi-Fi prob sensing can be integrated with feature sets. The adaptive lasso was implemented in feature selection. CO2 concentration, temperature, and Wi-Fi signal indicators are the most correlated features.
Cross-source sensing data fusion for building occupancy prediction with adaptive lasso feature filtering
Wang, Wei (author) / Hong, Tianzhen (author) / Xu, Ning (author) / Xu, Xiaodong (author) / Chen, Jiayu (author) / Shan, Xiaofang (author)
Building and Environment ; 162
2019-07-13
Article (Journal)
Electronic Resource
English
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