A New Power Load Forecasting Model Based on Rough Set and Artificial Neural Network
基于粗糙集与神经网络的电力负荷新型预测模型

For a multifactor power load prediction problem, this paper attempts to propose a new power load forecasting model-Rough Set Radial Basis Function Networks(abbreviated to RSRBFN), by combining rough set and artificial neural network. Rough set approaches and the conception of information entropy are employed to reduce factors of loads and input variables of the input layer with no changing classification quality of samples. Typical samples can been gotten by expurgating redundant information. These typical samples will been used to reduce neurons of the hidden layer and train neural network. The non-linear optimal problem about learning connection weights could been transformed to linear programming so as to optimize structure of neural network, improves the computing efficiency, the prediction accuracy and the potential practicability of Radial Basis Function Networks(abbreviated to RBFN). The feasibility, effectiveness and practicability \$f\$ RSRBFN was verified by experiments comparing RBAN with the proposed approach.