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S. H. Oudjana
A. Hellal



Authors and WSEAS

S. H. Oudjana
A. Hellal


WSEAS Transactions on Business and Economics


Print ISSN: 1109-9526
E-ISSN: 2224-2899

Volume 15, 2018

Notice: As of 2014 and for the forthcoming years, the publication frequency/periodicity of WSEAS Journals is adapted to the 'continuously updated' model. What this means is that instead of being separated into issues, new papers will be added on a continuous basis, allowing a more regular flow and shorter publication times. The papers will appear in reverse order, therefore the most recent one will be on top.

Volume 15, 2018


New Particle Swarm Neural Networks Model Based Long Term Electrical Load Forecasting in Slovakia

AUTHORS: S. H. Oudjana, A. Hellal

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ABSTRACT: Long-term load forecasting accuracy is very important for electrical power systems. This paper explores the application of new model using neural networks (NN) and Particle Swarm Optimization (PSO) to study the design of load forecasting systems for many years ahead using historical loads databases of Slovakia power systems. In this study, instead of the method of back-propagation of the gradient, the optimization technique by swarms of particles is well tested for training neural network that optimizes the forecast error. Simulations were run and the results are discussed showing that New Hybrid Technique (PSO-NN) is capable to decrease the load forecasting error.

KEYWORDS: Load Forecasting, Neural Networks, Particle Swarm Optimization

REFERENCES:

[1] Sandjib, M. Short term load forecasting using computational intelligence methods, Master Thesis. National Institute of Technology of Rourkela, 2008.

[2] Vincent , L. Modèles semi-paramétriques appliqués à la prévision des séries temporelles Cas de la consommation d’électricité, PhD Thesis, Université Rennes 2, 2007.

[3] AlRashidi, M.R.- EL-Naggar, K.M. Long term electric load forecasting based on particle swarm optimization, Applied Energy, 87, No. 1, 2010, pp.320-326.

[4] Park, D.C - El-Sharkawi, M.A. Marks, R.J.- Atlas, L.E- Damborg, M.J. Electric load forecasting using an artificial neural network, IEEE Trans. on Power Systems, Vol. 6, 1991, pp. 442-449.

[5] Lee, K..Y.- Tae, C. - Chao, C.K. – June, H.P. Neural network architectures for short-term WSEAS TRANSACTIONS on BUSINESS and ECONOMICS S. H. Oudjana, A. Hellal E-ISSN: 2224-2899 16 Volume 15, 2018 load forecasting, Neural Networks. IEEE World Congress on Computational Intelligence, Vol. 7, 27 June -2 July,1994, pp.4724-4729.

[6] Bakirtzis, A.G. - Petridis, V. - Kiartzis, S.J. - Alexiadis, M.C. - Maissis, A.H. - A neural network short term load forecasting model for the Greek power system., IEEE Trans. on Power Systems, Vol. 11. No. 2, 2012, pp. 858- 863.

[7] Hongbin, W. - Wei, l.C. Load Forecasting for Electrical Power System Based on BP Neural Network, First International Workshop on Education Technology and Computer Science, 2009, pp. 702 - 705.

[8] Manoj, K. Short-term load forecasting using artificial neural network techniques, Bachelor of technology, National Institute of Technology of Rourkela, 2009.

[9] Zhang, C. – Lin, M. – Tang, M. BP Neural Network Optimized with PSO Algorithm for Daily Load Forecasting, International Conference on Information Management, Innovation Management and Industrial Engineering. Vol. 3, (19-21Dec 2008), pp.82- 85.

[10] Vincent, G. Optimisation par Essaim Particulaire, Mémoire Adaptative et Recherche Tabou. PhD Thesis, Université Rennes 2, 2008

[11] https://www.entsoe.eu/.

WSEAS Transactions on Business and Economics, ISSN / E-ISSN: 1109-9526 / 2224-2899, Volume 15, 2018, Art. #3, pp. 13-17


Copyright © 2018 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution License 4.0