Dynamic user preference parameters selection and energy consumption optimization for smart homes using deep extreme learning machine and bat algorithm
The advancements in electronic devices have increased the demand for the internet of things (IoT) based smart homes, where the connecting devices are growing at a rapid pace. Connected electronic devices are more common in smart buildings, smart cities, smart grids, and smart homes. The advanceme...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2020
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/85263/1/85263_Dynamic%20user%20preference%20parameters.pdf http://irep.iium.edu.my/85263/ https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9253632 |
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| Summary: | The advancements in electronic devices have increased the demand for the internet of
things (IoT) based smart homes, where the connecting devices are growing at a rapid pace. Connected
electronic devices are more common in smart buildings, smart cities, smart grids, and smart homes. The
advancements in smart grid technologies have enabled to monitor every moment of energy consumption
in smart buildings. The issue with smart devices is more energy consumption as compared to ordinary
buildings. Due to smart cities and smart homes’ growth rates, the demand for efficient resource management
is also growing day by day. Energy is a vital resource, and its production cost is very high. Due to that,
scientists and researchers are working on optimizing energy usage, especially in smart cities, besides
providing a comfortable environment. The central focus of this paper is on energy consumption optimization
in smart buildings or smart homes. For the comfort index (thermal, visual, and air quality), we have used
three parameters, i.e., Temperature (◦F), illumination (lx), and CO2 (ppm). The major problem with the
previous methods in the literature is the static user parameters (Temperature, illumination, and CO2); when
they (parameters) are assigned at the beginning, they cannot be changed. In this paper, the Alpha Beta filter
has been used to predict the indoor Temperature, illumination, and air quality and remove noise from the data.
We applied a deep extreme learning machine approach to predict the user parameters. We have used the Bat
algorithm and fuzzy logic to optimize energy consumption and comfort index management. The predicted
user parameters have improved the system’s overall performance in terms of ease of use of smart systems,
energy consumption, and comfort index management. The comfort index after optimization remained near
to 1, which proves the significance of the system. After optimization, the power consumption also reduced
and stayed around the maximum of 15-18wh |
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