Short-Term Load Forecasting of a Distribution Transformer using Self-Organizing Fuzzy Neural Networks

  • Karim Beiranvand Jundi-Shapur University of Technology
  • Seyyedeh Fatemeh Molaeezadeh Jundi-Shapur University of Technology

Abstract

The distribution transformer Load forecasting is very essential in the control of future smart grids and an economical interfacing of Distributed Resources (DRs) to distribution networks. A distribution transformer connects DRs to the main grid. Exact distribution transformer load forecasting makes an economical DRs scheduling possible. In this regard, this paper firstly introduces a new Self-Organizing Fuzzy Neural Network (SOFNN). Then, it applies SOFNN to perform a five-minute load forecasting for a real-life distribution transformer in Lorestan Electric Power Distribution Company (LEPDC). Simulation results for active and reactive powers show that the proposed SOFNN outperforms ANFIS.

References

[1] S. Paoletti, M. Casini, A. Giannitrapani, A. Facchini, A. Garulli, and A. Vicino, “Load forecasting for active distribution networks,” in Innovative Smart Grid Technologies (ISGT Europe), 2011 2nd IEEE PES International Conference and Exhibition on, 2011, pp. 1-6.
[2] G. E. Box and G. M. Jenkins, Time series analysis: forecasting and control, revised ed: Holden-Day, 1976.
[3] C. Chen, Y. Tzeng, and J. Hwang, “The application of artificial neural networks to substation load forecasting,” Electric Power Systems Research, vol. 38, pp. 153-160, 1996.
[4] Z. Yun, Z. Quan, S. Caixin, L. Shaolan, L. Yuming, and S. Yang, “RBF neural network and ANFIS-based short-term load forecasting approach in real-time price environment,” Power Systems, IEEE Transactions on, vol. 23, pp. 853-858, 2008.
[5] D.-x. Niu, H.-Q. Wang, and Z.-H. Gu, “Short-term load forecasting using general regression neural network,” in Machine Learning and Cybernetics, 2005. Proceedings of 2005 International Conference on, 2005, pp. 4076-4082.
[6] M. Tripathi, K. Upadhyay, and S. Singh, “Short-term load forecasting using generalized regression and probabilistic neural networks in the electricity market,” The Electricity Journal, vol. 21, pp. 24-34, 2008.
[7] C. Xia, J. Wang, and K. McMenemy, “Short, medium and long term load forecasting model and virtual load forecaster based on radial basis function neural networks,” International Journal of Electrical Power & Energy Systems, vol. 32, pp. 743-750, 2010.
[8] A. Alves Da Silva, U. d. P. Rodrigues, A. R. Reis, and L. S. Moulin, “NeuroDem-a neural network based short term demand forecaster,” in Power Tech Proceedings, 2001 IEEE Porto, 2001, p. 6 pp. vol. 2.
[9] S. Kiartzis, C. Zoumas, A. Bakirtzis, and V. Petridis, “Data pre-processing for short-term load forecasting in an autonomous power system using artificial neural networks,” in Electronics, Circuits, and Systems, 1996. ICECS'96., Proceedings of the Third IEEE International Conference on, 1996, pp. 1021-1024.
[10] K. S. Yap, I. Z. Abidin, C. P. Lim, and M. S. Shah, “Short term load forecasting using a hybrid neural network,” in Power and Energy Conference, 2006. PECon'06. IEEE International, 2006, pp. 123-128.
[11] K. Nose-Filho, A. Lotufo, and C. Minussi, “Short-term multinodal load forecasting in distribution systems using general regression neural networks,” in PowerTech, 2011 IEEE Trondheim, 2011, pp. 1-7.
[12] K. Nose-Filho, A. Lotufo, and C. Minussi, “Preprocessing data for short-term load forecasting with a general regression neural network and a moving average filter,” 2011 IEEE PES Trondheim PowerTech: The Power of Technology for a Sustainable Society, POWERTECH 2011, 2011.
[13] Q. Zhang and T. Liu, “Research on the mid-long term electric load forecasting based on fuzzy rules,” in 2010 2nd IEEE International Conference on Information Management and Engineering, 2010, pp. 461-463.
[14] N. Amjady, “Short-term bus load forecasting of power systems by a new hybrid method,” Power Systems, IEEE Transactions on, vol. 22, pp. 333-341, 2007.
[15] P. Dash, S. Mishra, S. Dash, and A. Liew, “Genetic optimization of a self organizing fuzzy-neural network for load forecasting,” in Power Engineering Society Winter Meeting, 2000. IEEE, 2000, pp. 1011-1016.
[16] H. Mao, X.-J. Zeng, G. Leng, Y. Zhai, and J. Keane, “Short-term load forecasting based on self-organizing fuzzy neural networks,” in Fuzzy Systems Conference, 2007. FUZZ-IEEE 2007. IEEE International, 2007, pp. 1-6.
[17] S. Osowski and K. Siwek, “The selforganizing neural network approach to load forecasting in the power system,” in Neural Networks, 1999. IJCNN'99. International Joint Conference on, 1999, pp. 3401-3404.
[18] S. F. Molaeezadeh, “development of type-2 fuzzy systems and its application to predict acute hypotensive”, PhD Dissertation, Biomedical Engineering Department, Amirkabir University of Technology, 2013.
Published
2016-07-18
How to Cite
Beiranvand, K., & Molaeezadeh, S. F. (2016). Short-Term Load Forecasting of a Distribution Transformer using Self-Organizing Fuzzy Neural Networks. Majlesi Journal of Energy Management, 5(2). Retrieved from http://journals.iaumajlesi.ac.ir/em/index/index.php/em/article/view/252
Section
Articles