灰色神经网络模型GNNM(1,1)在城市年用电量预测中的应用

针对城市电力系统年用电量增长的特点，将灰色神经网络模型GNNM(1，1)引入城市年用电量预测。GNNM(1，1)模型是把灰色方法与神经网络有机结合起来，对复杂的不确定性问题进行求解所建立的模型。该模型通过建立一个BP网络，来映射GM(1，1)模型的灰色微分方程的解。GNNM(1，1)模型采用BP学习算法，网络经训练收敛后就可进行城市年用电量预测。算例计算表明，与灰色预测方法相比，GNNM(1，1)模型具有更强的适应性和更高的预测精度，适用于城市年用电量预测。

Application of grey neural network model GNNM (1,1) in city electricity demand forecasting

According to the speciality of electricity demand development in a city, the grey neural network model GNNM (1,1) was introduced into the field of city electricity demand forecasting in this paper. The GNNM (1,1) model is the combination of grey system and neural network, which can solve the complex uncertain problems. The GNNM (1,1) model builds a kind of BP neural network, which can map the solution to the grey differential equation of GM (1,1) model, then the model is trained by using BP algorithm. City electricity demand is forecasted after the GNNM (1,1) model is convergent. The forecasting results demonstrate that the GNNM (1,1) model has higher adaptability and forecast precision for city electricity demand forecasting.