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An Intelligent IoT-based Home Automation for Optimization of Electricity Use
Indian Inst Informat Technol Kottayam IIITK, Dept Comp Sci & Engn, Kottayam, India..
Indian Inst Informat Technol Kottayam IIITK, Dept Comp Sci & Engn, Kottayam, India..
Indian Inst Informat Technol Kottayam IIITK, Dept Comp Sci & Engn, Kottayam, India..
Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT). Univ Pretoria, Dept Elect Elect & Comp Engn, Pretoria, South Africa..ORCID iD: 0000-0002-2763-8085
2023 (English)In: Przeglad Elektrotechniczny, ISSN 0033-2097, E-ISSN 2449-9544, Vol. 99, no 9, p. 123-127Article in journal (Refereed) Published
Abstract [en]

The world is gearing towards renewable energy sources, due to the numerous negative repercussions of fossil fuels. There is a need to increase the efficiency of power generation, transmission, distribution, and use. The proposed work intends to decrease household electricity use and provide an intelligent home automation solution with ensembled machine learning algorithms. It also delivers organized information about the usage of each item while automating the use of electrical appliances in a home. Experimental results show that with XGBoost and Random Forest classifiers, electricity usage can be fully automated at an accuracy of 79%, thereby improving energy utilization efficiency and improving quality of life of the user.

Abstract [pl]

Świat zmierza w kierunku odnawialnych Ĩródeá energii ze wzglĊdu na liczne negatywne reperkusje paliw kopalnych. Istnieje potrzeba zwiĊkszenia efektywnoĞci wytwarzania, przesyáu, dystrybucji i uĪytkowania energii. Proponowane prace mają na celu zmniejszenie zuĪycia energii elektrycznej w gospodarstwach domowych i zapewnienie inteligentnego rozwiązania automatyki domowej z poáączonymi algorytmami uczenia maszynowego. Dostarcza równieĪ zorganizowanych informacji na temat uĪytkowania kaĪdego elementu, jednoczeĞnie automatyzując korzystanie z urządzeĔ elektrycznych w domu. Wyniki eksperymentów pokazują, Īe dziĊki klasyfikatorom XGBoost i Random Forest zuĪycie energii elektrycznej moĪna w peáni zautomatyzowaü z dokáadnoĞcią do 79%, poprawiając w ten sposób efektywnoĞü wykorzystania energii i poprawiając jakoĞü Īycia uĪytkownika. 

Place, publisher, year, edition, pages
Wydawnictwo SIGMA-NOT, sp. z.o.o. , 2023. Vol. 99, no 9, p. 123-127
Keywords [en]
Smart home automation, Ensembled Machine learning algorithms, Microcontroller, Proximity Sensors
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:mau:diva-63020DOI: 10.15199/48.2023.09.23ISI: 001058501400023Scopus ID: 2-s2.0-85174916449OAI: oai:DiVA.org:mau-63020DiVA, id: diva2:1803423
Available from: 2023-10-09 Created: 2023-10-09 Last updated: 2024-02-05Bibliographically approved

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Malekian, Reza

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