【作者】 康丽峰；

【导师】 尹成群； 孙伟；

【作者基本信息】 华北电力大学（河北） ， 信号与信息处理， 2007， 硕士

【摘要】 针对电力负荷受到多因素的影响以及典型训练样本选择问题,提出了一种基于数据挖掘技术的新型短期负荷预测方法。首先利用小波奇异性检测原理和软阈值细节消噪法对原始负荷数据进行剔除异常值预处理。其次将处理后的负荷序列利用小波变换分解为不同的频率分量。对于每一分量,利用信息熵与主成分分析法联合对负荷影响因素约简;利用动态聚类法由少到多自动确定网络隐层节点数和训练样本集;在采用动态聚类和最小二乘初始化网络的基础上,通过蚁群算法优化网络参数。最后,通过小波重构得到真正的日负荷预测结果。利用本文方法对实际的地区电网进行了测试,结果表明,该方法具有较高的预测精度和较强的适应能力。更多还原

【Abstract】 For a multifactor power load prediction problem and typical training sample selection, a new method for Short-Term Load Forecasting (STLF) based on data mining is put forward. First of all, through adjusting amplitude of wavelet modulus maxima and processing the wavelet decomposed detail signal by soft threshold based on wavelet analysis and singularity theory, fault data in original loads are eliminated. Then, through wavelet transform, the processed load sequence is decomposed into different frequency parts. For each part, information entropy and principal component analysis are integrated to reduce load influential factors; dynamic clustering analysis is used to automatically determine hidden nodes and training set; ant colony optimization algorithm is employed to optimize the network parameters initialized by dynamic clustering and least square method. Finally, the eventual forecasted results are obtained through wavelet restructure. The testing results of STLF in actual power network show that the proposed method possesses higher forecasting accuracy and better adaptability.更多还原