Abapour, Mehdi

Anvari-Moghaddam, Amjad

Mohammadi-Ivatloo, Behnam

Moradzadeh, Arash

Zeinal-Kheiri, Sevda

English

Considering the importance of energy and thenecessity of its management, this paper examines residentialenergy disaggregation/non-intrusive load monitoring. SupportVector Machine (SVM) has been proposed as one of the mostpowerful machine learning applications to solve this problem.The advantage of this method over other methods is the featureextraction of data and their classification based on recognizedpatterns. The proposed method is conducted on two REDD andAMPds datasets, which are related to real-world measurements.In the proposed method, SVM is trained by each of thecharacteristics of a particular electrical appliance. Then, thetrained network shows the closest recognition to identify thegiven electrical appliance and predict the total powerconsumption of homes. The accuracy obtained for the datasetsshows the applicability of the proposed method for loaddisaggregation

Support Vector Machine-Assisted Improvement Residential Load Disaggregation

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https://vbn.aau.dk/ws/files/334267660/2.pdf

Support Vector Machine-Assisted Improvement Residential Load Disaggregation

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