基于高维数据优化聚类的长周期峰谷时段划分模型研究

为了使峰谷时段划分结果客观反映出各时段的负荷差异,且能够在一个较长的时间周期(例如1a)内适用,本文提出一种以数据样本集高维化处理和K均值聚类分析相结合的时段划分模型。首先,通过数据高维化的处理方法构建涵盖较长时间周期(例如1a)内所有负荷信息的数据样本集;其次,以K均值算法为聚类分析工具,在高维数据样本集上构建峰谷时段划分模型。最后,结合某区全年负荷数据,对所构建的模型进行算例仿真,在验证模型的合理性基础上,最终输出时段划分结果。

Research on the Partition Model of Long Period Peak and Valley Time Based on High Dimensional Data Clustering

In this paper, in order to make the results of peak and valley time division reflect the load difference of each period objectively and be applicable in a long period of time (e.g., 1 year), a time division model is presented by combining processing of high-dimension data sampling set and K-means clustering analysis. First of all, a data sample set covering all load information within a long period of time (e.g., 1 year) is built by using the processing method of high-dimension data. Secondly, the peak and valley time division model based on the high-dimension data sample set is built by using K- means clustering analysis. Finally, the numerical simulation of proposed model is carried out by combining the load data of the whole year in certain district, and the final division result of the peak and valley time can be output on the basis of verifying the rationality of the model.