基于信息论与混合聚类分析的短期负荷预测方法研究

谢真桢; 杨秀; 张鹏; 徐磊

谢真桢,杨秀,张鹏,徐磊.基于信息论与混合聚类分析的短期负荷预测方法研究[J].电测与仪表,2017,54(19):.
XIE Zhenzhen,YANG Xiu,ZHANG Peng,XU Lei.Study on short-term load forecasting based on information theory and mixed clustering analysis[J].Electrical Measurement & Instrumentation,2017,54(19):.

基于信息论与混合聚类分析的短期负荷预测方法研究

Study on short-term load forecasting based on information theory and mixed clustering analysis

DOI：

中文关键词: 信息论欧氏距离余弦相似度聚类分析支持向量机短期负荷预测

英文关键词:information theory Euclidean distance cosine similarity cluster analysis support vector machine(svm) short-term load forecasting

基金项目:国家电网公司科技项目(520940150010,52094015001L)，国家自然科学基金项目（51407114）

作者中文名	作者英文名	单位
谢真桢	XIE Zhenzhen	上海电力学院电气工程学院
杨秀	YANG Xiu	上海电力学院电气工程学院
张鹏	ZHANG Peng	国网上海市电力公司电力科学研究院
徐磊	XU Lei	上海电力学院电气工程学院

摘要点击次数: 1257

中文摘要:

影响短期负荷预测的因素众多,传统方法仅根据各种因素与负荷的相关性大小依次选取所需个数而不考虑因素之间的相关性冗余,而且传统聚类分析中欧氏距离不能很好的度量负荷曲线形态上的相似性。因此,通过欧氏距离与余弦相似度混合度量,对各用户负荷特性曲线聚类,然后用信息论方法在9种关联因素中选取考虑相关性冗余的最优组合,将与待预测用户同类的负荷及其关联因素数据集作为训练样本,建立支持向量机预测模型。通过对上海某地实际样本数据的分析,证明该方法预测结果平均相对误差为1.46%,相对误差控制在1%以内的概率达到78.79%,具有较好的实用性。

英文摘要:

Short-term load forecasting is affected by many factors. Euclidean distance in the traditional clustering analysis can"t measure the similarity between the load curves well. Therefore, Euclidean distance mixed with the cosine similarity is used to cluster the load curves of all kinds of users. Then information theory method is used to select the optimal combination in 9 kinds of associated factors .The user load and its associated factors whose kind is the same with the user who we want to predict its load are taken as the training sample data sets to establish the support vector machine forecasting model. Through the analysis of the actual sample data in a certain area of Shanghai, results proved that the average relative error of this method is 1.46% and 78.79% of the relative errors are below 1%. It has a better practicability.

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