短期负荷预测的组合数据挖掘算法

给出了一种短期电力负荷预测的组合数据挖掘算法.通过日负荷特性分析,在设定长度的最近历史日期中选择与预测日天气最相似的为基准日,通过该模式下天气相似日的相关影响因素差异与相应负荷变化率关联规则挖掘建模,挖掘模型算法采用C4.5和CART算法的基于BP网络加权组合,算法还基于范例推理给出节假日调整因子校正节假日的影响,设计出一种高精确度短期负荷预测系统.实际应用结果表明该组合算法预测精确度高、效果良好.

Short-term Electric Load Forecasting with Combined Data Mining Algorithm

The combined data-mining Algorithm of short-term electric load forecasting is presented. Based on analyzing the characteristic of daily power loads, the most similar day recently to the forecasting-target day is selected as the benchmark day. With this way of arranging data, the data mining is used to obtain the relationship of the difference of influent factors and the load variance rate between the benchmark day and the forecasting target day, and also, the mining model algorithm is the weight combination of C4.5 and CART based on BP network. The holiday influence on loads is dealt with by the holiday justifying factor based on case reasoning. The highly accurate short-term load forecasting system is designed with above solutions. The accuracy and effectiveness of the combining algorithm proposed has been proved with the actual application.