Journal
IEEE SIGNAL PROCESSING MAGAZINE
Volume 35, Issue 5, Pages 100-110Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/MSP.2018.2842096
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Funding
- National Research Foundation (NRF) of Singapore via the Green Buildings Innovation Cluster (GBIC) [NRF2015ENC-GBICRD001-028]
- SUTD-MIT International Design Center (IDC)
- NSFC [61750110529]
- U.S. National Science Foundation [CNS-1702808, ECCS-1549881]
- Div Of Electrical, Commun & Cyber Sys
- Directorate For Engineering [1549881] Funding Source: National Science Foundation
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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, IoT-based 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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