Mahdi Farhadi

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It is of vital importance to use proper training data to perform accurate short-term load forecasting (STLF) based on artificial neural networks. The pattern of the loads which are used for the training of Kohonen Self Organizing Map (SOM) neural network in STLF models should be of the highest similarity with the pattern of the electric load of the forecasting day. In this paper, an electric load classifier model is proposed which relies on the pattern recognition capability of SOM. The performance of the proposed electric load classifier method is evaluated by Iran electric grid data. The proposed method requires a very few number of training samples for training the Kohonen neural network of the STLF model and can accurately predict electric load in the network.


Short-term load forecasting, similar sampling process, Kohonen self-organizing map, pattern recognition, electric load classification, load classifier

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G. Dudek, "Artificial immune system with local feature selection for short-term load forecasting," IEEE Transactions on Evolutionary Computation, vol. 21, no. 1, pp.116-130, Feb. 2017.

H. Quan, D. Srinivasan, A. Khosravi, "Short-term load and wind power forecasting using neural network-based prediction intervals", IEEE Transactions on Neural Networks and Learning Systems, vol. 25, no. 2, pp. 303-315, Feb. 2014.

S. V. Verdú, M. O. García, C. Senabre, A. G. Marín, F. J. G. Franco, “Classification, filtering, and identification of electrical customer load patterns through the use of self-organizing maps,” IEEE Transactions on Power Systems, vol. 21, no. 4, pp. 1672-1682, 2006.

S. Zhong, K. S. Tam,”Hierarchical classification of load profiles based on their characteristic attributes in frequency domain,” IEEE Transactions on Power Systems, vol. 30, no. 5, pp. 2434-2441, Sep. 2015.

L. Du, D. He, R. G. Harley, and T. G. Habetler, “Electric load classification by binary voltage-current trajectory mapping,” IEEE Transactions on Smart Grids, vol. 7, no. 1, pp. 358-365, Jan 2016.

L. Du, J. A. Restrepo, Y. Yang, R. G. Harley, T. G. Habetler, “Nonintrusive, self-organizing, and probabilistic classification and identification of plugged-in electric loads,” IEEE Transactions on Smart Grid, vol. 4, no. 3, pp. 1371-1380, Sept. 2013.

S. Patra, L. Bruzzone, “A novel SOM-SVM-based active learning technique for remote sensing image classification,” IEEE Transactions on Geoscience and Remote Sensing, vol. 52, no. 11, pp. 6899-6910, Nov. 2014.

Ervin D. Varga, Sándor F. Beretka, Christian Noce, and GianlucaSapienza,"Robust Real-Time Load Profile Encoding and Classification Framework for Efficient Power Systems Operation", IEEE Transactions on Power Systems, vol. 30, no. 4, pp.1897-1904, July 2015.

Kaustav Basu, Vincent Debusschere, Seddik Bacha, Ujjwal Maulik, and Sanghamitra Bondyopadhyay, "Nonintrusive Load Monitoring: A Temporal Multilabel Classification Approach", IEEE Transactions on Industrial Informatics, vol. 11, no. 1, pp. 262-270, Feb. 2015.

Dawei He, Liang Du, Yi Yang, Ronald Harley, and Thomas Habetler, "Front-End Electronic Circuit Topology Analysis for Model-Driven Classification and Monitoring of Appliance Loads in Smart Buildings", IEEE Transactions on Smart Grid, vol. 3, no. 4, pp.2286-2293, Dec. 2012.

Antti Mutanen, Maija Ruska, Sami Repo, and Pertti Järventausta," Customer Classification and Load Profiling Method for Distribution Systems", IEEE Transactions on Power Delivery, vol. 26, no. 3, pp. 1755-1763, July 2011.

O.E. Dragomira, F. Dragomirb, M. Radulescuc, Matlab Application of Kohonen Self- Organizing Map to Classify Consumers’ Load Profiles. ITQM, Computer Science 31, pp. 474 - 479, 2014.

M. Farhadi, S.M. Moghaddas Tafreshi, Effective Model for Next Day Load Cureve Forecasting Based Upon Combination of Perceptron and Kohonen ANNs Applied to Iran Power Network. In Proceedings of the IEEE, INTELEC, Rome, Italy, Sep 30 - Oct 4, 2007.

S. Valero, J. Aparicio, C. Senabre, M. Oritz, J. Sancho, and A. Gabaldon, "Comparative analysis of self organizing maps vs. multilayer perceptron neural networks for short-term load forecasting," In Proceedings of the International Symposium on Modern Electric Power Systems (MEPS), 2010, no. 1, pp. 1-5.

J. Llanos, D. Sáez, R. Palma-Behnke, A. Núñez, G. Jiménez-Estévez. Load Profile Generator and Load Forecasting for a Renewable Based Microgrid Using Self Organizing Maps and Neural Networks, In Proceedings of the IEEE WCCI, 2012.

K.I. Kimi, C. H. Jini, Y. K. Leei, K. D.Kim, K.H.Ryui. Forecasting wind power generation patterns based on SOM clustering. Awareness Science and Technology (iCAST), 2011.

M. Farhadi, M. Farshad," A fuzzy inference self-organizing-map based model for short term load forecasting" In Proceedings of the 17th Conference on Electrical Power Distribution Networks (EPDC), 2-3 May 2012, Tehran, Iran.


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