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首页> 外文期刊>Building and Environment >Cross-source sensing data fusion for building occupancy prediction with adaptive lasso feature filtering
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Cross-source sensing data fusion for building occupancy prediction with adaptive lasso feature filtering

机译：利用自适应套索特征滤波的跨源传感数据融合，用于建筑物占用预测

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摘要

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.
机译：融合各种感测数据源可以显着提高建筑物占用检测的准确性和可靠性。由于其计算和技术复杂性，很少研究融合环境传感器和无线网络信号。这项研究旨在提出一个集成的自适应套索模型，该模型能够提取环境和Wi-Fi探针双感应源的关键数据特征。通过快速特征提取和过程简化，该方法旨在提高占用检测模型的计算效率。为了验证所提出的模型，进行了现场实验，以检查两种占用数据分辨率（实时和四级占用分辨率）。结果表明，在所有十二个特征中，八个特征最相关。实时占用率的平均绝对误差可以降低到2.18，四级占用率的F1_accuracy约为84.36％。

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