应用动态泛回归神经网络的在线负荷预测

Load On-line Prediction based on Dynamic GRNN Neural Network

电力系统负荷变化受多方面因素的影响,因此负荷曲线呈现出强烈的非线性。为实现非线性的电力负荷在线预测,应用递推更新的样本数据集训练泛回归神经网络,构成动态泛回归神经网络。该动态神经网络训练方便快捷,能够满足在线预测的实时性的要求。仿真表明预测值较观测值有一定滞后,但均能尾随观测值而变化,达到了预期的目的。

There are various facts for the change of power system load. The load curve indicates strong nonlinearities. This paper proposes a method of on-line prediction of power system load,therefore ,general regression neural network （GRNN） is modified to meet the requirements as above. It is trained by using the recursively updated sample dale sets and thus GRNN became dynamic neural network. Training the dynamic neural network is convenient and fast, and so it can be applied to on-line prediction. The results of simulation experiments show that there is a little lag between the prediction profile and real profile, but the former vary with the latter. The expected aim is reached.