基于事例推理模糊神经网络的中压配电网短期节点负荷预测

CBRFNN-BASED SHORT-TERM NODAL LOAD FORECASTING FOR MIDDLE VOLTAGE DISTRIBUTION NETWORKS

根据认知科学理论,在并行分布处理(PDP)模型基础上,提出了一种基于事例推理的模糊神经网络(CBRFNN)。分析了CBRFNN的原理,定义了CBRFNN的基本结构,并提出一种混合(有监督/无监督)学习算法,使得CBRFNN具备了很好的泛化能力。CBRFNN中的所有节点通过快速、增量式的学习过程动态生成,并可通过网络自组织来有效抵御坏数据的影响。所提方法很好地解决了中压配电网短期节点负荷预测这类信息不完备、不精确问题。

A case-based-reasoning fuzzy-neural-network (CBRFNN) is presented based on cognitive science and Parallel Distributed Processing (PDP) model. The principle of CBRFNN is analyzed, the elementary architecture of CBRFNN is defined, and a hybrid (supervised/unsupervised) learning algorithm is also proposed, which equips CBRFNN with a good generalization capability. All nodes in a CBRFNN are created dynamically by the rapid and incremental learning procedure. CBRFNN can withstand the effect of bad data effectively through network self-organizing. The proposed method can solve the short-term nodal load-forecasting problem for middle voltage distribution network, which belongs to the kind of problems that are characterized by incomplete and inaccurate information.