层次匹配范例推理在短期负荷预测中的应用  

Case-based reasoning for short-term load forecasting based on hierarchy matching

作　　者：王志勇[1] 曹一家[1] 

机构地区：[1]浙江大学电气工程学院

出　　处：《浙江大学学报（工学版）》2007年第9期1598-1603,共6页Journal of Zhejiang University：Engineering Science

基　　金：高等学校博士学科点基金资助项目(20030335003)

摘　　要：提出了一个改进的范例推理系统来解决电力系统短期负荷预测问题,该系统将范例推理、自组织映射以及模糊粗糙集方法进行了有效的结合.使用模糊粗糙集方法确定了范例的表示、组织方法,并通过自组织映射对历史范例进行聚类.将新问题所对应的范例与各个聚类中心进行匹配,得到最相似聚类,再在该聚类中进行二次匹配,对得到的最相似范例集进行重用、修正,从而得到最终预测结果.使用模糊粗糙集方法可以进行范例属性和匹配权重的合理选择,同时使用自组织映射对历史范例进行聚类,可以减少范例匹配次数和匹配时间.使用该方法不仅可以合理利用历史范例,而且可以通过属性选取、聚类来获取附加知识.实例验证和比较结果表明该负荷预测方法是有效可行的.An improved reasoning system was presented to solve the short-term load forecasting （STLF） problem in power system operation and planning by using case-based reasoning （CBR）, self-organizing map （SOM） and fuzzy-rough sets method. CBR is composed of the steps of case representation, indexing, retrieval, and adaptation, and the key idea in CBR involves the use of already existing knowledge about objects or situations to predict aspects of similar objects. SOM are trained as a cluster tool in order to organize the old cases with the purpose of speeding up the CBR process. Fuzzy-rough sets method further extends the rough set concept through the use of fuzzy equivalence classes and is presented as a tool to extract principal case attributes. This method uses not only case-specific knowledge of past problems, but also uses additional knowledge derived from the clusters of cases and provides a new way for selecting proper feature subset and feature weights. The testing results on a real power system show that the proposed system is feasible and promising for STLF.

关 键 词：负荷预测 范例推理 模糊粗糙集 自组织映射 数据挖掘 

分 类 号：TM734[电气工程—电力系统及自动化]