Design of a-based smart meters to monitor electricity usage in the household sector using hybrid particle swarm optimization - neural network

Muhammad Yusuf Yunus(1*), Marhatang Marhatang(2), Andareas Pangkung(3), Muhammad Ruswandi Djalal(4),

(1) State Polytechnic of Ujung Pandang
(2) State Polytechnic of Ujung Pandang
(3) State Polytechnic of Ujung Pandang
(4) State Polytechnic of Ujung Pandang
(*) Corresponding Author


The procedure is training and testing the nerves that will be made. Matlab software has a Neural Network tool, which in this study will be used. Load sampling data is used as input data for neural network training. As output / target load classification is used. Load classification method, which is 1 for TV load classification, 2 for fan load, 3 for iron load, 4 for water pump load, 5 for lamp load, 6 for dispenser load, and 7 for fan iron load combination. The total load is 6 single loads and 1 combination load. One load combination was chosen because, on the combination load characteristics after the fan has characteristics that are not the same as the others. Data sampling of the current of each load will be used as neural network training. Load data used is 30 samples or for 30 seconds, with every minute the data is taken. From the results of the training, it can be seen that the biggest training error is in the seventh data, namely the identification of the load on the classification of the fan-iron load. This is because the current pattern on the iron and fan with the iron or fan itself has almost the same characteristics. However, for this process networks will be used and then the PSO optimization method is used to reduce the error, in the next study. From the test results, it is shown that by varying the input current data of each load, the network has been able to identify well, even though in the data classification load 7, the load of the iron-fan combination still has a large error. This will be corrected in subsequent studies with Particle Swarm Optimization (PSO) algorithm optimization.


Smart Meter Monitoring Load Neural Networks Particle Swarm Optimization

Full Text:


Article Metrics

Abstract view : 529 times
PDF - 134 times


H. Koko, "Desain Smart Meter Untuk Memantau Dan Identifikasi Pemakaian Energi Listrik Pada Sektor Rumah Tangga Menggunakan Backpropagation Neural Network," ITS Surabaya, 2015.

G. W. Hart, "Nonintrusive Appliance Load Monitoring," presented at the Proceedings IEEE, 1992.

I. E. L. J. G. Roos, E. C. Lane, and G. P. Hanche "Using neural networks for non-intrusive monitoring of industrial electrical loads," presented at the Proceedings of IEEE Instrumentation and Measurement Technology Conference, 1994.

K. L. C. Laughman, R. Cox, S. Shaw, S. B. Leeb, L. Norford, and P. Armstrong, "Power Signature Analysis," presented at the IEEE Power & Energy Magazine, 2003.

T. R. f. D. o. E. C. C. Energy Consumption in United Kingdom, "Energy Consumption in United Kingdom, Technical Report for Department of Energy & Climate Change " 2010.

a. L. S. J. Uteley, "Domestic Energy Fact File," Technical Report for Building Research Establishment : Garston, UK., 2008.

S. K. K. N. Jian Liang, Gail Kendall, and John W. M. Cheng, "Load Signature Study—Part I: Basic Concept, Structure, and Methodology," presented at the IEEE Transactions On Power Delivery, 2010.

K. A. D. K.E Martinez, and J.A Laitner "Advanced Metering Initiatives and Residential Feedback Programs: A Meta-Review for Households Electricity-Saving Opportunities," Technical Report E105 for American Council for an Energy-Efficient Economy (ACEE), USA., 2010.

S. Kusumadewi, "Membangun Jaringan Syaraf Tiruan Menggunakan MATLAB & EXCEL LINK," Graha Ilmu, 2004.

M. H. Purnomo, dan Kurniawan, A, "Supervised Neural Networks dan Aplikasinya," Graha Ilmu, 2006.

Y.-Y. Hong and J.-H. Chou, "Nonintrusive energy monitoring for microgrids using hybrid self-organizing feature-mapping networks," Energies, vol. 5, pp. 2578-2593, 2012.


Copyright (c) 2019 International Journal of Artificial Intelligence Research


International Journal Of Artificial Intelligence Research

Organized by: Departemen Teknik Informatika STMIK Dharma Wacana
Published by: STMIK Dharma Wacana
Jl. Kenanga No.03 Mulyojati 16C Metro Barat Kota Metro Lampung
phone. +62725-7850671
Fax. +62725-7850671
Email: | 

View IJAIR Statcounter

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.