基于纵横交叉算法优化神经网络的负荷预测模型

Short-term load forecasting using neural network based on wavelets and crisscross optimization algorithm

为了解决传统BP神经网络对高频分量预测精度不高、泛化能力弱的缺点,提出了一种混合小波变换和纵横交叉算法(CSO)优化神经网络的短期负荷预测新方法。通过小波变换对负荷样本进行序列分解,对单支重构所得的负荷子序列采用纵横交叉算法优化的神经网络进行预测。最后叠加各子序列的预测值,得出实际预测结果。通过实际电网负荷预测表明,新模型能掌握冲击毛刺的变化规律,有效提高含大量冲击负荷地区的负荷预测精度,且预测模型具有较强泛化能力。

To overcome the defect of conventional BP neural network with low prediction accuracy for high-frequency component and weak generalization ability, this paper presents a hybrid technique combining wavelet transform and crisscross optimization（CSO） to optimize artificial neural network for short-term load forecasting. Wavelet transform is used to decompose the load series into different scales, after which, the neural network optimized by CSO is employed to forecast the load sub-sequences obtained by single reconstruction, and then, the values of all sub-sequences are added to get the actual forecasting results. A test for practical power system shows that the new model has stronger generalization ability and can grasp the change regulation of impact burr perfectly and improve the precision of forecasting with plenty of shock load effectively.