优化动态递归小波神经网络短期负荷预测模型  

Short-term Load Forecasting Model Based on Optimized Dynamic Recurrent Wavelet Neural Network

作　　者：张智晟[1] 段晓燕 李伟婕 龚文杰 孙雅明[3] 

机构地区：[1]青岛大学自动化工程学院,青岛266071 [2]青岛供电公司,青岛266002 [3]天津大学电气与自动化工程学院,天津300072

出　　处：《电力系统及其自动化学报》2009年第5期30-35,共6页Proceedings of the CSU-EPSA

基　　金：山东省教育厅科技计划项目(J07WJ10);青岛大学引进人才科研基金项目(063-06300520)

摘　　要：提出了优化动态递归小波神经网络(dynamic recurrent wavelet neural network,DRWNN)短期负荷预测模型。与常规小波神经网络相比,DRWNN有两个关联层,关联层节点起存储网络内部状态的作用;模型构造过程中增强了网络的前馈与反馈联接,形成多层次的网络递归。采用分布估计算法和遗传算法相融合对DRWNN进行优化,融合实质是在解空间"宏观"和"微观"两个层面进行寻优,可克服DRWNN陷入局部最小,提高DRWNN的泛化能力。对两类不同负荷系统日、周预测仿真测试,验证了模型能有效提高预测精度。An optimized DRWNN （dynamic recurrent wavelet neural network） model for STLF （short--term load forecasting） is constructed in this paper. Compared with conventional wavelet neural network, DRWNN owns two context layers, nodes of which can save internal state of network; The feed--forward connection and feedback connection are increased, which forms recursion from multi-level. The DRWNN is optimized by the combining estimation of distribution algorithm with genetic algorithm, essence of which is searching the opti- mal solution from microscopic and macroscopic level, and it can avoid DRWNN immersing in the local minimal points and improve generalization ability. Two kinds of load systems are used in case study, and the testing results show the proposed model can effectively improve the precision of STLF.

关 键 词：短期负荷预测 动态递归小波神经网络 分布估计算法 遗传算法 

分 类 号：TM715[电气工程—电力系统及自动化]