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Nonintrusive Load Monitoring (NILM) systems aim to estimate the individual appliance operation and consumption in a household from measurements at a single point, in order to motivate energy conservation behaviors. Power estimation based on appliance rated powers is not always accurate because of the voltage supply variations, thus misleading load disaggregation algorithms. In this paper we propose...
Nonintrusive Load Monitoring (NILM) provides information about the electrical power consumption per appliance in a house to manage the energy consumption. NILM requires measurements in only one point and algorithms to make load disaggregation. One approach is classifying characteristics of the appliance through machine learning techniques such as Support Vector Machines (SVM) and Artificial Neural...
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