Earth Worm Optimization for Home Energy Management System in Smart Grid

Ali, Muhammad; Abid, Samia; Ghafar, Asad; Ayub, Nasir; Arshad, Hafsa; Khan, Sajawal ur Rehman; Javaid, Nadeem

doi:10.1007/978-3-319-69811-3_52

Cited by 8 publications

(8 citation statements)

References 11 publications

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“…Similarly, we have used (5) for the computation of the difference and (6) for computing predicting position. ∆ = − (5) = + .…”

Section: B Alpha Beta Filtermentioning

confidence: 99%

“…The synchronization in terms of the demand and supply of electrical power is required to reduce smart homes' energy consumption. With smart cities' advancements, the traditional grids have also been replaced with smart grids, monitoring the energy demand and producing energy accordingly [5]. The prediction techniques are most commonly used to predict day-ahead energy pricing and energy prediction in smart grids and smart homes [6].…”

Section: Introductionmentioning

confidence: 99%

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Dynamic User Preference Parameters Selection and Energy Consumption Optimization for Smart Homes Using Deep Extreme Learning Machine and Bat Algorithm

et al. 2020

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“…Similarly, we have used (5) for the computation of the difference and (6) for computing predicting position. ∆ = − (5) = + .…”

Section: B Alpha Beta Filtermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Dynamic User Preference Parameters Selection and Energy Consumption Optimization for Smart Homes Using Deep Extreme Learning Machine and Bat Algorithm

et al. 2020

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“…Ali et al [74] have presented a simulated model for smart grid energy management and optimization using earthworm algorithm [75] and bacterial foraging algorithm [76,77]. Authors have used a pricing scheme so that the consumers get an idea of cost and use electricity accordingly and avoid usage of energy during peak hours to avoid the extra cost of electricity.…”

Section: Algorithms and Techniques For Energy Optimization Through Scmentioning

confidence: 99%

“…For shorter-range communications, Bluetooth is preferred in smart homes [120,121]. In the smart home's network, the energy management controller controls the electronic devices [74]. The electronic devices consume considerable energy depending on their nature, as every smart home has an air conditioner for cooling, a heater for heating, purifiers for air quality, and light for illumination.…”

Section: Technologies Used In Smart Homesmentioning

confidence: 99%

A Review on Energy Consumption Optimization Techniques in IoT Based Smart Building Environments

et al. 2019

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In recent years, due to the unnecessary wastage of electrical energy in residential buildings, the requirement of energy optimization and user comfort has gained vital importance. In the literature, various techniques have been proposed addressing the energy optimization problem. The goal of each technique is to maintain a balance between user comfort and energy requirements, such that the user can achieve the desired comfort level with the minimum amount of energy consumption. Researchers have addressed the issue with the help of different optimization algorithms and variations in the parameters to reduce energy consumption. To the best of our knowledge, this problem is not solved yet due to its challenging nature. The gaps in the literature are due to advancements in technology, the drawbacks of optimization algorithms, and the introduction of new optimization algorithms. Further, many newly proposed optimization algorithms have produced better accuracy on the benchmark instances but have not been applied yet for the optimization of energy consumption in smart homes. In this paper, we have carried out a detailed literature review of the techniques used for the optimization of energy consumption and scheduling in smart homes. Detailed discussion has been carried out on different factors contributing towards thermal comfort, visual comfort, and air quality comfort. We have also reviewed the fog and edge computing techniques used in smart homes.

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“…Untuk menamplikan total energi keseluruhan dari peralatan sebelum dan sesudah optimasi pada waktu tertentu, persamaan yang digunakan yaitu persamaan (4) dan (14).…”

Section: ) Menentukan Energy Consumption All Devicesunclassified

Desain dan Implementasi Smart Home Konsumsi Daya Rendah Menggunakan Algoritma Optimisasi Cuckoo-Earthworm

Alfafa¹,

Bilfaqih²

2020

JTITS

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Penggunaan energi merupakan hal yang paling penting pada sistem smart home, karena dengan energi yang kecil maka sistem smart home akan semakin efisien dan juga ekonomis. Konsumsi energi pada sistem smart home dioptimalkan dengan teknik penjadwalan peralatan secara real time berdasarkan harga listrik pada saat itu dan persentase kenyamanan pengguna dimana konsumsi daya setiap peralatan diatur untuk melakukan penghematan. Melalui pendekatan ini pengguna dapat menentukan tingkat kenyamanan secara fleksibel untuk melakukan penghematan tanpa mengurangi kenyamanan yang diperoleh dari setiap peralatan rumah tangga. Algoritma Cuckoo-Earthworm digunakan untuk proses penjadwalan peralatan secara real time. Sistem smart home dilengkapi dengan Raspberry Pi3 sebagai HUB controller dan Smart Plug yang digunakan untuk monitor energi yang digunakan pada setiap peralatan dan juga sebagai switch untuk melakukan penjadwalan. Komunikasi antara HUB controller dan setiap device menggunakan jaringan Z-Wave. Untuk user interface menggunakan Home Assistant. Pada implemetasi sistem smart home dengan daya rendah menggunakan algoritma Cuckoo-Eartworm kali ini didapatkan pengurangan biaya mencapai 41.39% dan energi mencapai 32.52% dari peralatan yang tidak terjadwal pada tingkat kenyamanan terendah.

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Earth Worm Optimization for Home Energy Management System in Smart Grid

Cited by 8 publications

References 11 publications

Dynamic User Preference Parameters Selection and Energy Consumption Optimization for Smart Homes Using Deep Extreme Learning Machine and Bat Algorithm

Dynamic User Preference Parameters Selection and Energy Consumption Optimization for Smart Homes Using Deep Extreme Learning Machine and Bat Algorithm

A Review on Energy Consumption Optimization Techniques in IoT Based Smart Building Environments

Desain dan Implementasi Smart Home Konsumsi Daya Rendah Menggunakan Algoritma Optimisasi Cuckoo-Earthworm

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