一种改进的短期负荷预测方法

采用三层BP型人工神经网络来建立短期负荷预测模型,将影响负荷的主要因素:系统的基本负荷、温度的差异、天气的改变和日期的类型(工作日与节假日)作为数据样本,进行网络的自我训练和学习,并且在训练和学习的过程中引入误差反方向传播算法(即BP算法)来修正神经网络的连接权重,从而达到对负荷预测模型的改良和完善,进一步贴近实际的负荷变化。同时,将因电力线路或设备的检修损失的负荷量也作为影响因素进行了考虑,从而得出更精确的预测负荷值。在实际的负荷预测算例中,上述的预测思路得到了较好的印证,其预测的精度也较高。

Three - hyer BP artificial neural network （ANN） is used to set up a short - term load forecast model. Some major factors such as basic load, temperature difference, weather change and different type of day （working day and holiday） are used as data sampies. Though the self- training and self- study of the network, BP method is introduced to revise the weight of ANN in the process of training and study. Thus the model of load forecast is improved. At the same time, the repairing of lines and equipments in power system is also considered, and the exact result is concluded due to this influencing factor. In practice, the above forecast method is proved to be prefect and its precision is very high too.