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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: Shah, Abdul Salam, Mohamad Nasir, Haidawati, Fayaz, Muhammad, Lajis, Adidah, Ullah, Israr, Shah, Asadullah
Format: Article
Language:English
Published: IEEE 2020
Subjects:
T Technology (General)
T10.5 Communication of technical information
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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spelling my.iium.irep.852632020-12-01T08:22:47Z http://irep.iium.edu.my/85263/ Dynamic user preference parameters selection and energy consumption optimization for smart homes using deep extreme learning machine and bat algorithm Shah, Abdul Salam Mohamad Nasir, Haidawati Fayaz, Muhammad Lajis, Adidah Ullah, Israr Shah, Asadullah T Technology (General) T10.5 Communication of technical information 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 IEEE 2020-11-20 Article PeerReviewed application/pdf en http://irep.iium.edu.my/85263/1/85263_Dynamic%20user%20preference%20parameters.pdf Shah, Abdul Salam and Mohamad Nasir, Haidawati and Fayaz, Muhammad and Lajis, Adidah and Ullah, Israr and Shah, Asadullah (2020) Dynamic user preference parameters selection and energy consumption optimization for smart homes using deep extreme learning machine and bat algorithm. IEEE Access, 8. pp. 204744-204762. E-ISSN 2169-3536 https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9253632 10.1109/ACCESS.2020.3037081
institution Universiti Islam Antarabangsa Malaysia
building IIUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider International Islamic University Malaysia
content_source IIUM Repository (IREP)
url_provider http://irep.iium.edu.my/
language English
topic T Technology (General)
T10.5 Communication of technical information
spellingShingle T Technology (General)
T10.5 Communication of technical information
Shah, Abdul Salam
Mohamad Nasir, Haidawati
Fayaz, Muhammad
Lajis, Adidah
Ullah, Israr
Shah, Asadullah
Dynamic user preference parameters selection and energy consumption optimization for smart homes using deep extreme learning machine and bat algorithm
description 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
format Article
author Shah, Abdul Salam
Mohamad Nasir, Haidawati
Fayaz, Muhammad
Lajis, Adidah
Ullah, Israr
Shah, Asadullah
author_facet Shah, Abdul Salam
Mohamad Nasir, Haidawati
Fayaz, Muhammad
Lajis, Adidah
Ullah, Israr
Shah, Asadullah
author_sort Shah, Abdul Salam
title Dynamic user preference parameters selection and energy consumption optimization for smart homes using deep extreme learning machine and bat algorithm
title_short Dynamic user preference parameters selection and energy consumption optimization for smart homes using deep extreme learning machine and bat algorithm
title_full Dynamic user preference parameters selection and energy consumption optimization for smart homes using deep extreme learning machine and bat algorithm
title_fullStr Dynamic user preference parameters selection and energy consumption optimization for smart homes using deep extreme learning machine and bat algorithm
title_full_unstemmed Dynamic user preference parameters selection and energy consumption optimization for smart homes using deep extreme learning machine and bat algorithm
title_sort dynamic user preference parameters selection and energy consumption optimization for smart homes using deep extreme learning machine and bat algorithm
publisher IEEE
publishDate 2020
url 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
_version_ 1685578555649425408
score 13.211869

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