TY - JOUR
T1 - Internet of Things for Green Building Management
T2 - Disruptive Innovations Through Low-Cost Sensor Technology and Artificial Intelligence
AU - Tushar, Wayes
AU - Wijerathne, Nipun
AU - Li, Wen Tai
AU - Yuen, Chau
AU - Poor, H. Vincent
AU - Saha, Tapan Kumar
AU - Wood, Kristin L.
N1 - Funding Information:
This work was supported, in part, by project NRF2015ENC-GBICRD001-028 funded by the National Research Foundation (NRF) of Singapore via the Green Buildings Innovation Cluster (GBIC) administered by the Building and Construction Authority (BCA)–Green Building Innovation Cluster (GBIC) Program Office; in part, by the SUTD-MIT International Design Center (IDC; idc.sutd.edu.sg); in part, by the grant NSFC 61750110529; and, in part, by the U.S. National Science Foundation under grants CNS-1702808 and ECCS-1549881. Any findings, conclusions, or opinions expressed in this document are those of the authors and do not necessarily reflect the views of the sponsors.
Publisher Copyright:
© 1991-2012 IEEE.
PY - 2018/9
Y1 - 2018/9
N2 - Buildings consume 60% of global electricity. However, current building management systems (BMSs) are highly expensive and difficult to justify for small-to medium-sized buildings. The Internet of Things (IoT), which can collect and monitor a large amount of data on different aspects of a building and feed the data to the BMS's processor, provides a new opportunity to integrate intelligence into the BMS for monitoring and managing a building's energy consumption to reduce costs. Although an extensive literature is available on, separately, IoTbased BMSs and applications of signal processing techniques for some building energy-management tasks, a detailed study of their integration to address the overall BMS is limited. As such, this article will address the current gap by providing an overview of an IoT-based BMS that leverages signal processing and machine-learning techniques. We demonstrate how to extract high-level building occupancy information through simple, low-cost IoT sensors and study how human activities impact a building's energy use-information that can be exploited to design energy conservation measures that reduce the building's energy consumption.
AB - Buildings consume 60% of global electricity. However, current building management systems (BMSs) are highly expensive and difficult to justify for small-to medium-sized buildings. The Internet of Things (IoT), which can collect and monitor a large amount of data on different aspects of a building and feed the data to the BMS's processor, provides a new opportunity to integrate intelligence into the BMS for monitoring and managing a building's energy consumption to reduce costs. Although an extensive literature is available on, separately, IoTbased BMSs and applications of signal processing techniques for some building energy-management tasks, a detailed study of their integration to address the overall BMS is limited. As such, this article will address the current gap by providing an overview of an IoT-based BMS that leverages signal processing and machine-learning techniques. We demonstrate how to extract high-level building occupancy information through simple, low-cost IoT sensors and study how human activities impact a building's energy use-information that can be exploited to design energy conservation measures that reduce the building's energy consumption.
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U2 - 10.1109/MSP.2018.2842096
DO - 10.1109/MSP.2018.2842096
M3 - Article
AN - SCOPUS:85053195106
VL - 35
SP - 100
EP - 110
JO - IEEE Audio and Electroacoustics Newsletter
JF - IEEE Audio and Electroacoustics Newsletter
SN - 1053-5888
IS - 5
M1 - 8454403
ER -