以某地区购网有功功率的负荷数据为背景，建立了３个BP神经网络负荷预测模型———SDBP，LMBP 及BRBP模型进行短期负荷预测工作，并对其结果进行比较。针对传统的BP算法具有训练速度慢，易陷入局部 最小点的缺点，采用具有较快收敛速度及稳定性的L-M（Levenberg-Marquardt）优化算法进行预测，使平均相对误 差有了很大改善，而采用贝叶斯正则化算法可以解决网络过度拟合，提高网络的推广能力。

Based on the load data of meritorious power of some area power system,  three BP ANN models,  namely SDBP, LMBP and BRBP Model,  are established to carry out the short-term load forecasting work, and the results are compared.  Since the traditional BP algorithm has some unavoidable disadvantages,  such as the low training speed and the possibility of being plunged into minimums local minimizing the optimized function,  an optimized L-M algorithm, which can accelerate the training of neural network and improve the stability of the convergence,  should be applied to forecast to reduce the mean relative error.  Bayesian regularization can overcome the over fitting and improve the generalization of ANN.