Computer Science ›› 2017, Vol. 44 ›› Issue (Z11): 133-135.doi: 10.11896/j.issn.1002-137X.2017.11A.027

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Short-term Load Forecasting of Power System Based on Alternating Particle Swarm BP Network

TANG Cheng-e   

  • Online:2018-12-01 Published:2018-12-01

Abstract: Short-term load forecasting is a key link in the normal operation of power system.Based on accurate load forecasting,an alternating particle swarm optimization algorithm was proposed to optimize the BP network model to predict short-term load in this paper.In order to determine the weight of the BP neural network,the weights of the BP neural network were optimized by using the alternating particle algorithm to reduce the error caused by the neural network prediction model to solve the short term load forecasting.The experimental results show that the optimized BP neural network prediction model is less accurate than the traditional BP neural network prediction model and closer to the actual power load.

Key words: Particle swarm optimization,BP neural networks,Short term load,Cross operation

[1] 刘晨晖.电力系统负荷预报理论和方法[M].哈尔滨:哈尔滨工业大学出版社,1987.
[2] XIE K G,LI C Y,ZHOU J Q.Research of the combination forecasting model for load based on artificial neural network [J].Proceedings of the CSEE,2002,22(7):85-89.
[3] YOU Y,SHENG W X,WANG S A.Short-term load forecasting using artificial immune network[J].Proceedings of the CSEE,2003,23(3):26-30.
[4] ZHENG G,LIU B,ZHOU Y,et al.Short-term load forecasting based on neural network[J].Journal of Xi’an University of Technology,2002,18(2):126-130.
[5] TAI N L,HOU Z J.New short-term load forecasting principle with the wavelet transform fuzzy neural network for the power systems[J].Proceedings of the CSEE,2004,24(1):24-29.
[6] YOU Y,SHENG W X,WANG S A.The study and application of the electric power system short-term load forecasting using a new model[J].Proceedings of the CSEE,2002,22(9):15-18.
[7] JIANG Y.Short-term load forecasting using a neural network based on fuzzy clustering[J].Power System Technology,2003,27(2):45-49.
[8] YAO L X,YAO J X,LI B Q,et al.Short-term load forecasting using neural network based on competitive learning classification[J].Power System Technology,2004,28(10):45-48.
[9] MCCLELLAND J L,RUMELHART D E.Parallel distributed processing [M].Cambridge:MIT Press,1986.
[10] LARSEN E V,MILLER N W,NILSSON S L,et al.Benefits of GTO-based compensation systems for electric utility applications[J].IEEE Trans on Power Delivery,1992,7(4):2056-2061.
[11] CEN W H,LEI Y K,XIE H.The application of the electric power system short-term load forecasting using artificial network and genetic algorithm[J].Automation of Electric Power Systems,1997,21(3):29-32.
[12] DING J W,SUN Y M.Short-term load forecasting using a neural network based on chaos learning algorithm[J].Automation of ElectricPower Systems,2000,24(2):32-35.
[13] YANGQ Y,SUN J G,ZHANG J Y,et al.A Hybrid Discrete Particle Swarm Algorithm for Open-Shop Problems[C]∥Proceedings of the 6th International Conference on Simulated Evolution And Learning (SEAL 2006).Hefei,China,LNCS 4247,2006:158-165.
[14] VAN DEN BERGH F.An Analysis of Particle Swarm Optimizers[D].Department of Computer Science,University of Pretoria,Pretoria,South Africa,2002.
[15] RAMESHKUMAR K,SURESH R K,Mohanasundaram K M.Discrete Particle Swarm Optimization (DPSO) Algorithm for Permutation Flowshop Scheduling to Minimize Makspan[C]∥Proc.ICNC 2005.LNCS 3612,2005:572-581.
[16] LIAN Z G,GU X S,JIAO B.A similar particle swarm optimization algorithm for permutation flowshop scheduling to minimize makespan[J].Applied Mathematics and Computation,2006,175(1):773-785.

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