Neural Networks Applied to Non-Intrusive Load Monitoring System
نویسندگان
چکیده
منابع مشابه
Non-Intrusive Appliances Load Monitoring System Using Neural Networks
A non-intrusive appliances load monitoring system has been developed to ascertain the behavior of each electrical appliance in a household by disaggregating the total household load demand. This system does not need to intrude into a house when metering power consumption of each appliance. Therefore, the system has significant cost advantages and is less troublesome to the customers. This paper...
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Non-Intrusive Load Monitoring (NILM) is a technique that determines the load composition of a household through a single point of measurement at the main power feed. Here we presented an unsupervised approach to determine the number of appliances in the household, their power consumption and the state of each one at any given moment.
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This paper presents the proposal of A new methodology for the identification of residential equipment in non-intrusive load monitoring systems that is based on a Convolutional Neural Network to classify equipment. The transient power signal data obtained at the time an equipment is connected in a residence is used as inputs to the system. The methodology was developed using data from a public d...
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Non-intrusive load monitoring (NILM) identifies used appliances in a total power load according to their individual load characteristics. In this paper we propose an evolutionary optimization algorithm to identify appliances, which are modeled as on/off appliances. We evaluate our proposed evolutionary optimization by simulation with Matlab, where we use a random total load and randomly generat...
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Field studies have demonstrated that it is possible to evaluate the state of many shipboard systems by analyzing the electrical power that is drawn by electromechanical actuators (Ramsey et al. 2005, DeNucci et al. 2005, Cox et al. 2006, Cox et al. 2007). This paper demonstrates how a device known as a non-intrusive load monitor (NILM) uses only electrical power data to detect several critical ...
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ژورنال
عنوان ژورنال: IEEJ Transactions on Electronics, Information and Systems
سال: 2002
ISSN: 0385-4221,1348-8155
DOI: 10.1541/ieejeiss1987.122.8_1351