Study of Super Short-Term Bus Load Forecasting Model Based on Similar Ranges

Abstract:

With the increasing demand of electricity dispatching and the request of energy conservation and emission reduction, the dispatching plan and operating of electric power has become more and more important. On one hand, electric power companies try to reduce power reserve as much as possible to increase the efficiency; on the other hand, some power reserve is necessary to deal with emergency and to ensure the safety and stability of grid. Therefore, this paper proposes a super short-term bus load forecasting model which is based on similar ranges to track the variation of weather and load. By using the method of fruit flies optimizing grey neural network in the real time, it can reduce the size of the network computing and solve the problem of divergence. Since the system based on the model has operated for one year, it proves that this model can meet the requirement of the precision for the electricity dispatching and adapt to the changes in different regions.

• Periodical: Applied Mechanics and Materials (Volume 492)
• Pages: 482-488
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.492.482
• Online since: January 2014
• Authors: Dong Xiao Niu, Yan Chao Chen, Jiao Fan, Qing Guo Ma, Qin Liang Tan
• Keywords: Bus Load Forecasting, Fruit Flies Optimization, Grey Neural Network, Similar Ranges

References

[1] Kassaei H.R., Keyhani, A.; Woung, T.; Rahman, M. A hybrid fuzzy, neural network bus load modeling and predication. IEEE Transactions on Power Systems. 1999, Vol. 14, No. 2.

DOI: 10.1109/59.761903

[2] Pyne R.A. Short-term bus load forecasting and its intended use in scheduled outage analysis[C]. IEEE Conference on Decision and Control including the 13th Symposium on Adaptive Processes, 1974: 593-597.

DOI: 10.1109/cdc.1974.270506

[3] Amjady N. Short-Term Bus Load Forecasting of Power System by a New Hybrid Method[J]. IEEE Transactions on Power Systems, 2008, 22(1): 333-341.

DOI: 10.1109/tpwrs.2006.889130

[4] YU Yi-xin, WU Jian-zhong. CBRFNN-Based Short-Term Nodal Load Forecasting for Middle Voltage Distribution Networks. Proceedings of the CSEE, 2005, Vol. 25 No. 12.

[5] LI Guang-zhen, LIU Wen-ying, YUN Hui-zhou, GAO Yu-hua. A New Data Preprocessing Method for Bus Load Forecasting . Power System Technology, 2012, Vol. 34 NO. 2.

[6] Senjyu T, Takara H, Uezato K, Funabashi T. One-hour-ahead load forecasting using neural network. IEEE Transactions on Power Systems, Feb. 2002. vol. 17, no. 1, 113~118.

DOI: 10.1109/59.982201

[7] Hippert H. S, Pedreira C. E, Souza R.C. Neural networks for short-term load forecasting: a review and evaluation. IEEE Transcations on Power Systems, Feb. 2001. vol. 16, no. 1.

DOI: 10.1109/59.910780

[8] Dingguo Chen, York M. Neural network based very short term load prediction[C]. Transmission and Distribution Conference and Exposition, 2008: 1-9.

DOI: 10.1109/tdc.2008.4517071

[9] Abdul Hamid M. B, Abdul Rahman T.K. Short Term Load Forecasting Using an Artificial Neural Network Trained by Artificial Immune System Learning Algorithm[C]. International Conference Computer Modelling and Simulation, 2010: 408-413.

DOI: 10.1109/uksim.2010.82

[10] Jordan J.A., Ukil A. Short term load forecasting using semi-parametric method and support vector machines[C]. AFRICON, 2009: 1-6.

DOI: 10.1109/afrcon.2009.5308213

[11] Bichpuriya Y., Rao M.S.S., Soman S.A. Combination approaches for short term load forecasting[C]. IPEC, 2010: 818-823.

DOI: 10.1109/ipecon.2010.5697079

[12] Salgado R.M., Ballini R., Ohishi T. An aggregate model applied to the short-term bus load forecasting problem[C]. Power Systems Conference and Exposition, 2009: 1-8.

DOI: 10.1109/psce.2009.4840223

[13] Ricardo Menezes Salgado. An Aggregate Model Applied To the Short-Term BusLoad Forecasting Problem. In: Rosangela Ballini and Takaaki 0hishi. 2009, 1-8.

[14] Ricardo Menezes Salgado, Takaaki Ohishi, Rosangela Ballini. A Short-Term BusLoad Forecasting System. International Conference on Hybrid Intelligent Systems. 2011, 55-60.

DOI: 10.1109/his.2010.5600075