一种新型短期负荷预测模型的研究及应用

THE STUDY AND APPLICATION OF THE ELECTRIC POWER SYSTEM SHORT-TERM LOAD FORECASTING USING A NEW MODEL

提出了一种将混沌和神经网络相结合的方法用于短期负荷预测。利用混沌方法重构相空间系统吸引子,用前向神经网络拟和吸引子上的全局整体映射,构成了混合预测模型。在实际应用中,将基于奇异值分解的噪声消减滤波算法应用到数据的预处理中,并用混沌学习算法来训练神经网络参数,预测结果表明了该方法的有效性。

A hybrid method based on chaos and neural network was used in the study of the electric power system short-term load forecasting. This article presents the use of chaos method to reconstruct attractors in phase spaces and a multi-layer feed forward neural network to fit the attractor抯 global map, to construct a hybrid prediction model. Moreover, this article shows the efficiency of a noise-suppressing method based on single value decomposition (SVD), and a new neural network learning algorithm, chaotic learning algorithm, is proposed. The result indicates that the method is effective.