Short-Term Load Forecasting using an Ensemble of Artificial Neural Networks: Chaharmahal Bakhtiari Case

Subject Areas : Electronics Engineering

E. Faraji ¹ , M. Mirzaeian ² , H. Parvin ³ , A. Chamkoorii ⁴ , Majid Mohammadpour ⁵

1 - 1Department of Computer Engineering, Khormooj Branch, Islamic Azad University, Khormooj, Iran 2Young Researchers and Elite Club, Nourabad Mamasani Branch, Islamic Azad University, Nourabad Mamasani, Iran [e-mail]
2 - Operation and Dispatching Department, Power Distribution Company, Chaharmahal Bakhtiari
3 - 1Department of Computer Engineering, Khormooj Branch, Islamic Azad University, Khormooj, Iran 2Young Researchers and Elite Club, Nourabad Mamasani Branch, Islamic Azad
4 - Department of Computer Engineering, Khormooj Branch, Islamic Azad University, Khormooj, Iran
5 - 1Department of Computer Engineering, Khormooj Branch, Islamic Azad University, Khormooj, Iran 2Young Researchers and Elite Club, Nourabad Mamasani Branch, Islamic Azad University, Nourabad Mamasani, Iran

Received: 2016-10-13 Accepted : 2017-07-12 Published : 2020-12-21

Keywords:

Abstract :

Short-term load forecasting is very important in electrical marketing. Load forecasting is dependent on climatic condition of every region and the previous structures of electrical consumption in that region; so we have accomplished this through employing climatic data (including temperature and pressure) and real load consumption of Chaharmahal Bakhtiari. We have evaluated our method using four machine learning algorithms: artificial neural networks (multilayer perceptron), ensemble of artificial neural networks, support vector machine and ensemble of support vector machine. Experimental results indicates that ensemble of artificial neural networks is superior to the others in the field of load consumption forecasting of Chaharmahal Bakhtiari.

References:

[1] سرلک، م.، ابراهیمی، ت.، توکلی، ا.، طلوع خیامی، م. و مقدس انگیزان، د. استفاده از شبکه­های عصبی برای پیش­بینی کوتاه مدت بار. بیست و پنجمین کنفرانس بین المللی برق، 1389.

[2] Amirarfaei, F., Menhaj, M.B and Barghinia, S., "A Combination of Pruning algorithm and Parallel Networks Structure to Increase the Generalization of Neural Networks Used for Short-Term Load Forecasting of Iran Power System", IEEE Int. conf. Asia-Pacific Power and Energy Engineering Conference (APPEEC, 2010), pp. 1-4.

[3] Amjady, N., " Short-term bus load forecasting of power systems by a new hybrid method" ,IEEE Trans. Power Syst., vol. 22, no. 1, Feb. 2007, pp. 333–341.

[4] Brockwell, P. J., Davis, R. A., "Introduction to Time Series and Forecasting", Berlin: Springer-Verlag, Mar. 2002.

[5] Elattar, E. E., Goulermas, J. Y., Wu, Q. H., "Electric load forecasting based on locally weighted support vector regression" ,IEEE Trans. Syst., Man Cyber. C, Appl. Rev., vol. 40, no. 4, July 2010, pp. 438–447.

[6] Felice, M. D., Yao, X., "Short-Term Load Forecasting with Neural Network Ensembles: A Comparative Study ", IEEE Computational Intelligence Magazine, July 2011, pp. 47-56.

[7] Farhadi, M and Moghaddas-Tafreshi S.M.,"Analysis of Effective Variables on Daily Electrical Load Curves of Iran Power Network", IEEE Int. conf. Industrial Technology, 2008, pp. 1-7.

[8] Fan, S., Chen, L., "Short-term load forecasting based on an adaptive hybrid method "  ,IEEE Trans. Power Syst., vol. 21, no. 1, 2006, pp. 392–401.

[9] Feinberg, E. A., Genethliou, D., "Load forecasting", in Applied Mathematics for Restructured Electric Power Systems: Optimization, Control, and Computational Intelligence, J. Chow, F. Wu, and J. Momoh, Eds. Berlin: Springer-Verlag, 2005, pp. 269–285.

[10] Hinojosa,A., Hoese, V. H., "Short-term load forecasting using fuzzy inductive reasoning and evolutionary algorithms" , IEEE Trans. Power Syst., vol. 25, no. 1, Feb. 2010, pp. 565–574.

[11] Piers, R.J., Adamson, K. Methodologies for Load Forecasting, Intelligent System, 3^rd International IEEE Conference, 2006, PP.800-806.

[12] Sapankevych, N., Sankar,   R., "Time series prediction using support vector machines: A survey", IEEE Comput. Intell. Mag., vol. 4, no. 2, 2009, pp. 24–38.

[13] Setiawan, A., Koprinska, I., Agelidis, V.G., et al. " Very short-term electricity load demand forecasting using support vector regression" ,Proc. Int. Joint Conf. on Neural Networks, 2009, pp. 2888–2894.

[14] Yun, Z., Quan, Z., Caixin, S., Shaolan, L., Yuming, L., Yang, S., "RBF neural network and ANFIS-based short-term load forecasting approach in real-time price environment" ,IEEE Trans. Power Syst., vol. 23, no. 3, Aug. 2008, pp. 853–858.

[15] Hansen, L., and Salamon, P., "Neural network ensembles", IEEE Trans. Pattern Anal. Mach. Intell., vol. 12, no. 10, 1990, pp. 993–1001.