Comparison of different BP neural network models for short-term load forecasting

摘要：

Short-term load forecasting(STLF) is of great importance for the safety and stabilization of grids. Based on the historical load data of meritorious power of some area in Guizhou power system, three BP neural networks in steepest descent algorithm back propogation neural network (SDBP), Levenberg -Marquardt algorithm back propogation neural network ( LMBP) and Bayesian regularization algorithm back propogation neural network ( BRBP ) models in 24 hours ahead prediction are compared. Since the traditional BP algorithm has some drawbacks such as slow training convergence speed and possibility of local minimizing the optimized function, an optimized L-M algorithm, which can improve the stability of convergence and accelerate the training speed of neural network has been applied to carry out load forecasting work to reduce the mean relative error. Bayesian regularization also be applied which can overcome and improve the generalization of neural network. The prediction precision of BRBP are superior to LMBP and SDBP,while BRBP has poor training speed than others.

关键词： Short-term load forecasting(STLF) steepest descent algorithm Levenberg-Marquardt Bayesian regularization

作者: Yuan Ning Yufeng Liu Huiying Zhang Qiang Ji

作者单位: College of Electrical Engineering Guizhou University Guiyang,Guizhou,China Department of Electrical,Computer, System and Engineering Rensselaer Polytechnic Institute Troy,New

会议类型: 国际会议

会议名称: 2010 IEEE International Conference on Intelligent Computing and Intelligent Systems(2010 IEEE 智能计算与智能系统国际会议 ICIS 2010)

会议地点: 厦门

会议语种:英文

页码: 435-438

在线出版日期: 2010-10-29（万方平台首次上网日期，不代表论文的发表时间）