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中文摘要：
就多因素影响而言，一个预报模型被造。BP 神经网络的 Thestructure 被设计，并且有免疫力的算法被使用优化它的网络结构和重量。在从在中国的一年 1980 ～ 2005 训练力量需求的数据以后，一个非线性的网络模型在在在它上有影响的力量需求和因素之间的关系上被获得，并且因此，上述建议方法被验证。同时，结果与基因算法优化的神经网络的那些相比。结果证明这个方法比基因算法优化的神经网络优异并且是时间系列预报的有效方法之一。
英文摘要：
Considering multi-factor influence, a forecasting model was built. The structure of BP neural network was designed, and immune algorithm was applied to optimize its network structure and weight. After training the data of power demand from the year 1980 to 2005 in China, a nonlinear network model was obtained on the relationship between power demand and the factors which had impacts on it, and thus the above proposed method was verified. Meanwhile, the results were compared to those of neural network optimized by genetic algorithm. The results show that this method is superior to neural network optimized by genetic algorithm and is one of the effective ways of time series forecast.
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