摘要
提出了采用经验模态分解(EMD)、动态神经网络与BP型神经网络相结合的混合模型进行电力系统短期负荷预测的方法。首先运用EMD将非平稳的负荷序列分解,然后根据分解后各分量的特点构造不同的动态神经网络对各分量分别进行预测,最后对各分量预测结果采用BP网络进行重构得到最终预测结果。仿真结果表明基于该方法的电力系统短期负荷预测具有较高的精度。
This paper proposed a hybrid model based on Empirical Mode Decomposition （EMD） ,dynamic neural network and BP nature network as a short-term load forecasting model. At first, based on EMD the load series is decomposed into different lots of calm series, then according to the features of decomposed components different dynamic neural network model, finally using the BP network to reconstruct the forecasted signals of the components and obtain the ultimate forecasting result. Simulink results show that the proposed forecasting method is accurate
出处
《电工电能新技术》
CSCD
北大核心
2008年第3期13-17,53,共6页
Advanced Technology of Electrical Engineering and Energy
关键词
短期负荷预测
经验模态分解
动态神经网络
short-term load forecasting
empirical mode decomposition （EMD）
dynamic neural network
