基于ESPRIT分解算法的短期电力负荷预测

摘要

According to the similarity of power load, this paper proposes an integrated load forecasting method based on estimating signal parameter via rotational invariance techniques (ESPRIT). First, the raw data signal is broken up into blocks through a spinning method, and then, it is separated into independent harmonic ingredients by using the least squares ESPRIT algorithm. In addition, before forecasting the power load with different models to get the final integrated forecasting load, we should cluster the ingredients for several categories by K-means clustering. ESPRIT algorithm which has high frequency resolution, is not requested to the synchronized sampling, and it can reduce the dimension data matrix. A better forecast is got by comprehensive forecasting method. Finally, MATLAB simulations indicate that the method is proved to be more stable, accurate and effective. This work is supported by the Science and Technology Supporting Program Project of Sichuan Province (No. 2012GZ0009).%电力负荷具有一定的周期相似性，为此，提出一种基于子空间旋转矢量不变技术(ESPRIT)的综合负荷预测方法。对电力负荷数据进行移位平移处理构造出满足子空间不变性的数据矩阵，利用最小二乘法ESPRIT原理进行谐波检测，提取出各主要频率分量成分。利用K均值聚类法把提取的分量根据频率特点分为不同类型，之后建立不同预测模型对各部分进行独立负荷预测，最终得到综合的预测负荷值。ESPRIT算法具有较高的频谱分辨率，可降低原数据维数，且综合预测法能针对不同成分有更好的预测。最后仿真也证明了该方法预测的准确性及有效性。