| Title |
Internet of Things for Green Building Management Disruptive innovations through low-cost sensor technology and artificial intelligence |
| ID_Doc |
30565 |
| Authors |
Tushar, W; Wijerathne, N; Li, WT; Yuen, C; Poor, HV; Saha, TK; Wood, KL |
| Title |
Internet of Things for Green Building Management Disruptive innovations through low-cost sensor technology and artificial intelligence |
| Year |
2018 |
| Published |
Ieee Signal Processing Magazine, 35, 5 |
| DOI |
10.1109/MSP.2018.2842096 |
| Abstract |
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. |
| Author Keywords |
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| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:000443991800012 |
| WoS Category |
Engineering, Electrical & Electronic |
| Research Area |
Engineering |
| PDF |
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