MATEC Web Conf.
Volume 218, 2018The 1st International Conference on Industrial, Electrical and Electronics (ICIEE 2018)
|Number of page(s)||6|
|Section||Control Electronics, Circuits, and Systems|
|Published online||26 October 2018|
Steady State Modification Method Based On Backpropagation Neural Network For Non-Intrusive Load Monitoring (NILM)
Department of Electrical Engineering, Faculty of Engineering, Universitas Indonesia, Depok, Indonesia
Corresponding author: email@example.com
Household electric power sector is highlighted as one of significant contributors to national energy consumption. To reduce electric energy usage in this sector, a technique called Non-Intrusive Load Monitoring (NILM) has been developed recently. NILM is a load disaggregating and monitoring tool that can be used to identify the daily usage behavior of individual electric appliance. Different to conventional method, NILM promises the reduction of sensor deployment significantly. NILM commonly uses either transient or steady state signal. Based on load/appliance signal condition, many NILM’s research results have been published. In this paper, steady state modification method of backpropagation neural network (NN) is applied for developing NILM. We use steady state signal to disaggregate the sum of load power signal. In the proposed method, NN is explored for feature extraction of electric power consumption of individual appliance. The presented method is powerful for load power signal which has almost same value. To verify the effectiveness of proposed method, data provided by tracebase.org has been used. The presented method can be applied for local data. It is obvious from simulation results that the proposed method could improve the recognition rate of appliances until 100 %.
© The Authors, published by EDP Sciences, 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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