The forecasting results demonstrate that the GNNM (1, 1) model has higher adaptability and forecast precision for city electricity demand forecasting.
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。
Study modelling thought, network configuration, majorize GNNM(1,1) mode method and learning algorithm of GNNM(1,1) mode combined grey system theory and neural network.
研究了灰色系统理论与神经网络组合的灰色神经网络GNNM（1，1）模型的建模思想、网络结构及其优化GNNM（1，1）模型的方法和学习算法；
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.
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。
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