TY - JOUR
T1 - Indoor Occupancy Detection Based on Environmental Data Using CNN-XGboost Model
T2 - Experimental Validation in a Residential Building
AU - Mohammadabadi, Abolfazl
AU - Rahnama, Samira
AU - Afshari, Alireza
N1 - Funding Information:
This research work was conducted as part of a project called SmartVENT. The Energy Technology Development and Demonstration Program (EUDP) financially supported this work (Journal No. 64018-0501).
Publisher Copyright:
© 2022 by the authors.
PY - 2022/11
Y1 - 2022/11
N2 - Indoor occupancy prediction can play a vital role in the energy-efficient operation of building engineering systems and maintaining satisfactory indoor climate conditions at the lowest possible energy use by operating these systems on the basis of occupancy data. Many methods have been proposed to predict occupancy in residential buildings according to different data types, e.g., digital cameras, motion sensors, and indoor climate sensors. Among these proposed methods, those with indoor climate data as input have received significant interest due to their less intrusive and cost-effective approach. This paper proposes a deep learning method called CNN-XGBoost to predict occupancy using indoor climate data and compares the performance of the proposed method with a range of supervised and unsupervised machine learning algorithms plus artificial neural network algorithms. The comparison is performed using mean absolute error, confusion matrix, and F1 score. Indoor climate data used in this work are CO2, relative humidity, and temperature measured by sensors for 13 days in December 2021. We used inexpensive sensors in different rooms of a residential building with a balanced mechanical ventilation system located in northwest Copenhagen, Denmark. The proposed algorithm consists of two parts: a convolutional neural network that learns the features of the input data and a scalable end-to-end tree-boosting classifier. The result indicates that CNN-XGBoost outperforms other algorithms in predicting occupancy levels in all rooms of the test building. In this experiment, we achieved the highest accuracy in occupancy detection using inexpensive indoor climate sensors in a mechanically ventilated residential building with minimum privacy invasion.
AB - Indoor occupancy prediction can play a vital role in the energy-efficient operation of building engineering systems and maintaining satisfactory indoor climate conditions at the lowest possible energy use by operating these systems on the basis of occupancy data. Many methods have been proposed to predict occupancy in residential buildings according to different data types, e.g., digital cameras, motion sensors, and indoor climate sensors. Among these proposed methods, those with indoor climate data as input have received significant interest due to their less intrusive and cost-effective approach. This paper proposes a deep learning method called CNN-XGBoost to predict occupancy using indoor climate data and compares the performance of the proposed method with a range of supervised and unsupervised machine learning algorithms plus artificial neural network algorithms. The comparison is performed using mean absolute error, confusion matrix, and F1 score. Indoor climate data used in this work are CO2, relative humidity, and temperature measured by sensors for 13 days in December 2021. We used inexpensive sensors in different rooms of a residential building with a balanced mechanical ventilation system located in northwest Copenhagen, Denmark. The proposed algorithm consists of two parts: a convolutional neural network that learns the features of the input data and a scalable end-to-end tree-boosting classifier. The result indicates that CNN-XGBoost outperforms other algorithms in predicting occupancy levels in all rooms of the test building. In this experiment, we achieved the highest accuracy in occupancy detection using inexpensive indoor climate sensors in a mechanically ventilated residential building with minimum privacy invasion.
KW - CNN-XGboost
KW - Indoor climate data
KW - Machine learning
KW - Mechanical ventilation
KW - Occupancy detection
KW - Residential buildings
KW - CNN-XGboost
KW - Indoor climate data
KW - Machine learning
KW - Mechanical ventilation
KW - Occupancy detection
KW - Residential buildings
UR - http://www.scopus.com/inward/record.url?scp=85141871442&partnerID=8YFLogxK
U2 - 10.3390/su142114644
DO - 10.3390/su142114644
M3 - Journal article
AN - SCOPUS:85141871442
SN - 2071-1050
VL - 14
JO - Sustainability (Switzerland)
JF - Sustainability (Switzerland)
IS - 21
M1 - 14644
ER -