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Integrated sensor data processing for occupancy detection in residential buildings
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DOI：10.1016/j.enbuild.2021.110810.png)
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Abstract
En 中文
Based on the data from U.S. Energy Information Administration (EIA), the total annual energy consumed by buildings in the United States has increased by 325% over the past 70 years. Many commercial buildings utilize a building management system (BMS) and occupancy sensors to better control heating, ventilation, and air conditioning (HVAC) systems. However, the complex and costly installation process of occupancy sensors prolongs the return on investment for the residential sector. This paper presents a cost-effective approach to occupancy detection utilizing a two-layer detection scheme based on data obtained from multiple non-intrusive sensors (temperature and motion). The sensor data were consumed by multiple heuristic models (lower layer) for recognizing a set of human activities (door handle touch, water usage, and motion near the door area). As non-intrusive sensors, such as temperature sensors, may lead to less accurate occupancy information, a data fusion scheme using machine learning (upper layer) is utilized to holistically validate any individual sensor. The proposed two-layer methodology enhances the validity and reliability of occupancy detection. The human activities data was used to train and test four machine learning models (Random Forest, Decision Tree, K-Nearest Neighbor, and Support Vector Machine). The proposed occupancy detection system was installed in a 62 m(2) living lab. Four temperature sensors and one motion sensor were used to collect the environmental information for 54 days. The validity of the proposed detection system was verified by the accuracy and the F1-score of each model. In all machine learning models, the two-layer detection system showed significant improvements to the accuracy and the F1-score over the current state-of-the-art approach with the same data. As such, the proposed work demonstrated similar or improved level of the accuracy (95%) and Fl-score (95%) over other works, while using reduced sensor density. (C) 2021 Elsevier B.V. All rights reserved.
Keywords:
Decision tree
Machine learning
Occupancy
Co-simulation
IoT
CPS
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