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文章摘要
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
Received:November 20, 2016  Revised:January 11, 2017
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)
Author NameAffiliationE-mail
XIE Zhenzhen College of Electric Engineering,Shanghai University of Electric Power xzz90s@126.com 
YANG Xiu* College of Electric Engineering,Shanghai University of Electric Power 1539625968@qq.com 
ZHANG Peng State Grid electric power research institute of Shanghai 1793521313@qq.com 
XU Lei College of Electric Engineering,Shanghai University of Electric Power 944469597@qq.com 
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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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