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Internet of Things for Green Building Management: Disruptive Innovations Through Low-Cost Sensor Technology and Artificial Intelligence

Published on Sep 1, 2018in IEEE Signal Processing Magazine7.602
· DOI :10.1109/MSP.2018.2842096
Wayes Tushar16
Estimated H-index: 16
(UQ: University of Queensland),
Nipun Wijerathne4
Estimated H-index: 4
+ 4 AuthorsKristin L. Wood39
Estimated H-index: 39
(NUS: National University of Singapore)
Sources
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, 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.
  • References (28)
  • Citations (16)
References28
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#1Wen-Tai Li (SUTD: Singapore University of Technology and Design)H-Index: 9
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Effective control of air conditioning systems (ACs) has the potential of significant electricity savings and demand response management for an entire power system. In this context, this paper demonstrates some key experimental results on controlling the electricity consumption of ACs. In particular, the degree to which energy can be throttled for energy management purposes without affecting the end user's comfort level is described. The testbed was set up in a residential building, in which the ...
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Energy disaggregation is the task of segregating the aggregate energy of the entire building (as logged by the smartmeter) into the energy consumed by individual appliances. This is a single channel (the only channel being the smart-meter) blind source (different electrical appliances) separation problem. The traditional way to address this is via stochastic finite state machines (e.g., factorial hidden Markov model). In recent times, dictionary learning-based approaches have shown promise in ad...
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#2Chau Yuen (SUTD: Singapore University of Technology and Design)H-Index: 49
Last. Kristin L. Wood (SUTD: Singapore University of Technology and Design)H-Index: 39
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#1Weicong Kong (USYD: University of Sydney)H-Index: 9
#2Zhao Yang Dong (UNSW: University of New South Wales)H-Index: 61
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Residential load forecasting has been playing an increasingly important role in modern smart grids. Due to the variability of residents’ activities, individual residential loads are usually too volatile to forecast accurately. A long short-term memory-based deep-learning forecasting framework with appliance consumption sequences is proposed to address such volatile problem. It is shown that the forecasting accuracy can be notably improved by including appliance measurements in the training data....
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Ideally, in the method about people counting based on multi-feature regression, the features, such as weighted blob area and perimeter, should have a linear relationship with the number of people in the scene. However, although the overall linear trend, due to the existence of occlusion, the foreground extraction errors and other factors, the local presents nonlinear characteristics. Gauss process regression is very suitable for linear features with local nonlinearity, so it is widely used at pr...
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Increasing cost and demand of energy has led many organizations to find smart ways for monitoring, controlling and saving energy. A smart Energy Management System (EMS) can contribute towards cutting the costs while still meeting energy demand. The emerging technologies of Internet of Things (IoT) and Big Data can be utilized to better manage energy consumption in residential, commercial, and industrial sectors. This paper presents an Energy Management System (EMS) for smart homes. In this syste...
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