摘要
提高非正常目的负荷预测精度是当前负荷预测工作的难点。文中提出了一种基于知识库的事先判别突变并做出适当处理的预测流程,介绍了利用数据挖掘的决策树技术建立知识库的方法,并给出了几种典型的非正常日修正模型。最后,通过对长时期负荷预测数据的统计分析,说明了新方法的有效性和实用性。
The current difficulty of short-term load forecasting is the forecasting of abnormal days' load. This paper puts forward a forecasting flow based on the knowledge bases, to prefigure the abnormity and make proper treatment. It also introduces the method of constructing the knowledge base using decision tree technique of data mining, and gives several practical amending models of typical abnormal days. In the end, it illuminates the new'method's effectiveness and practicability through the statistical analysis of the load forecasting data over a long period of time.
出处
《电力系统自动化》
EI
CSCD
北大核心
2004年第3期53-57,共5页
Automation of Electric Power Systems
关键词
负荷预测
数据挖掘
决策树
知识库
非正常日
load forecasting
data mining
decision tree
knowledge base
abnormal days
