Intelligent Load Identification of Household-Smart Meters Using Multilevel Decision Tree and Data Fusion Techniques
Yazarlar (7)
Mohammed Hasan Aldulaimi Al-Mustaqbal University, Irak
Ibrahim Najem
Al-Turath University, Irak
Tabarak Ali Abdulhussein Imam Ja'afar Al-Sadiq University, Irak
M. H. Ali National University Of Science And Technology, Irak
Asaad Shakir Hameed Mazaya University College, Irak
M. Altaee Al-Farahidi University, Irak
Doç. Dr. Hatıra GÜNERHAN Kafkas Üniversitesi, Türkiye
Makale Türü Özgün Makale (SCOPUS dergilerinde yayınlanan tam makale)
Dergi Adı Journal of Intelligent Systems and Internet of Things
Dergi ISSN 2769-786X Scopus Dergi
Makale Dili İngilizce Basım Tarihi 01-2023
Cilt / Sayı / Sayfa 9 / 1 / 24–33 DOI 10.54216/JISIoT.090102
Makale Linki http://www.scopus.com/inward/record.url?eid=2-s2.0-85169095470&partnerID=MN8TOARS
UAK Araştırma Alanları
Fen Bilimleri ve Matematik
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