运用决策支持对象实现短期电力负荷预测

运用微软通用的决策支持对象(DSO),结合区域电网气象负荷数据库设计了决策树形式的数据挖掘模型并实现了日负荷预测系统.在描述了DSO分层结构特性之后,分析研究了日负荷预测的决策树数据挖掘模型构造过程并给出了程序化实现方法,进一步实现了通过决策树算法的负荷预测过程.实际使用的效果统计分析结果表明本系统达到并超过实用标准,具有智能自适应、自学习和全过程自动化,通用可靠以及准确率高等特性,是值得推广的方便实用型负荷预测工具.

SHORT-TERM LOAD FORECASTING BY USE OF DECISION SUPPORT OBJECTS

Using general decision support objects(DSO) of Microsoft Corporation a data mining model with the form of decision tree is designed and a daily load forecasting system is implemented according to the weather-load database of regional power network. After describing the DSO hierarchy structure, the constructing process of decision-tree data mining models for daily load forecasting is analyzed and the programming way for this model is given, furthermore, the load forecasting process by decision tree algorithm is implemented. The results of actual application and statistic analysis show that the presented system is intelligent, adaptive, versatile, reliable and accurate, it possesses the features such as self-study and full automatic load forecasting, therefore as an easy and practical load forecasting tool, this system is worth wide-spreading.