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Intelligent Load Identification of Household-Smart Meters Using Multilevel Decision Tree and Data Fusion Techniques   
Yazarlar
Mohammed Hasan Aldulaimi
Al-Mustaqbal University, Iraq
Ibrahim Najem
Al-Turath University, Iraq
Tabarak Ali Abdulhussein
Imam Ja'afar Al-Sadiq University, Iraq
M. H. Ali
National University of Science and Technology, Iraq
Asaad Shakir Hameed
Mazaya University College, Iraq
M. Altaee
Al-Farahidi University, Iraq
Doç. Dr. Hatıra GÜNERHAN Doç. Dr. Hatıra GÜNERHAN
Kafkas Üniversitesi, Türkiye
Özet
The DTA-LI system's fusion data method is crucial in the monitoring of appliance loads for the purposes of improving energy efficiency and management. Common home electrical devices are identified and classified from smart meter data through the analysis of voltage and current variations, allowing for the measurement of energy usage in residential buildings. A load identification system based on a decision tree algorithm may infer information about the residents of a building based on their energy usage habits. Better power savings rates, load shedding management, and overall electrical system performance are the results of the clusters' ability to capture families' purchasing patterns and geo-Demographic segmentation. The DTA-LI system's fusion data method presents a promising avenue for improving residential buildings' energy performance and lowering their carbon footprint, especially in light of the widespread use of smart meters in recent years.
Anahtar Kelimeler
Appliance load monitoring | Data Fusion Techniques | decision tree algorithm | household | smart meter
Makale Türü Özgün Makale
Makale Alt Türü SCOPUS dergilerinde yayımlanan tam makale
Dergi Adı Journal of Intelligent Systems and Internet of Things
Dergi ISSN 2769-786X
Makale Dili İngilizce
Basım Tarihi 01-2023
Cilt No 9
Sayı 1
Sayfalar 24 / 33
Doi Numarası 10.54216/JISIoT.090102