基于粗糙集理论建立短期电力负荷神经网络预测模型

选择模型输入变量和网络结构是采用神经网络建立负荷预测模型的关键步骤，该文提出了一种基于粗糙集理论的解决方法。此方法采用粗糙集理论对各种影响负荷预测的因素变量进行识别，以此确定预测模型的输入变量；在此基础上通过属性约简和属性值约简获得推理规则集，再以这些推理规则构筑神经网络预测模型，并采用加动量项的BP学习算法对网络进行优化。此方法能遵循一定的理论原则建立负荷预测模型以避免盲目性。最后通过实例计算证明此方法是可行和有效的。

APPLYING ROUGH SET THEORY TO ESTABLISH ARTIFICIAL NEURAL NETWORKS FOR SHORT TERM LOAD FORECASTING

Choosing input variable and networks architecture are key processes for modeling short term load forecast by artificial neural networks, in this paper a method based on rough set theory is proposed to deal with them. In the proposed approach, the key factors that affect the load forecasting are firstly identified by rough set theory and then the input variables of forecast model can be determined. On the basis of the process mentioned aboves a set of inference rules can been obtained through reductive mining process of attributes and attribute values, then a neural networks of load forecast model is established on the rule set and BP-algorithm is adopt to optimize the networks. The method indicates that load forecast model can be established according some theoretical principles and avoiding blindness. A practical application is given at last to demonstrate the usefulness of the novel method.