电测与仪表2012，Vol.49Issue(5)：48-51,84,5.
基于局部均值分解与神经网络的短期负荷预测
Power System Short-term Load Forecasting Based on Local Mean Decomposition and Artificial Neural Network
兰华 1常家宁 1周凌 1王冰 1张镭2
作者信息
- 1. 东北电力大学电气工程学院,吉林吉林132012
- 2. 长春市建设工程交易中心信息部,长春130000
- 折叠
摘要
Abstract
Short-term load forecasting is the basis of the power system dispatching and operation. In order to improve the short-term power load precision, a novel approach for short-term load forecasting is presented based on local mean decomposition (LMD) and artificial neural network (ANN). First of all, based on LMD the load series is decomposed into different lots of 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. The, simulation results show that the LMD-BP neural network method has higher precision of prediction than the EMD-BP neural network method, also verify the feasibility and efficiency of this method.关键词
短期负荷预测/局部均值分解/人工神经网络Key words
short-term load forecasting/ local mean decomposition/ artificial neural network分类
信息技术与安全科学引用本文复制引用
兰华,常家宁,周凌,王冰,张镭..基于局部均值分解与神经网络的短期负荷预测[J].电测与仪表,2012,49(5):48-51,84,5.
