基于粗糙集属性约简算法和支持向量机的短期负荷预测

Short-Term Load Forecasting Based on Attribute Reduction Algorithm of Rough Sets and Support Vector Machine

结合粗糙集和支持向量机两种智能算法提出了短期负荷预测模型。首先根据历史数据建立属性决策表,通过属性约简算法对数据进行挖掘,找到影响负荷的核心因素,然后将它们作为支持向量机的输入矢量来预测负荷。算例结果表明,新模型与按经验选取输入矢量的传统支持向量机模型相比,预测精度有了很大的提高且更适用于短期负荷预测。

A short-term load forecasting model based on two integrated intelligent algorithms, i.e., attribute reduction algorithm of rough sets and support vector machines （SVM）, is proposed. At first, according to historical data a attribute decision table is built up and the data mining is performed by means of attribute reduction algorithm, thus the kernel factors influencing loads is determined and using them as the input vectors of SVM the load forecasting is conducted. Forecasting results of calculation examples show that comparing with traditional SVM model that chooses input vectors in the light of experience the forecasting accuracy is evidently improved and is more suitable to short-term load forecasting.