【作者】 孙伟； 卢建昌； 孟明；

【Author】 SUN Wei, LU Jian-chang, MENG Ming (School of Business Administration. North China Electric Power University, Baoding 071003, China.

【机构】 华北电力大学工商管理学院；

【摘要】 建立了基于时间序列的支持向量机短期电价预测模型．支持向量机法采用结构最小化原则,能在小样本学习的基础上,对其他样本进行快速、准确的拟合预测,具有更好的泛化能力．此外,在短期电价预测模型中除了引入历史负荷,系统旋转备用,竞争发电容量等影响因素外,还将历史清算电价时间序列也作为支持向量机的输入属性参数,建立了提前一天的电力市场清算电价预测模型．仿真结果证明该模型具有较好的预测精度．更多还原

【Abstract】 Time series based support vector machine (SVM) model is provided for short term price forecasting. The Structure Risk Minimization (RSM) principle is embedded into the SVM, so on the basis of learning by fewer samples the presented model can conduct fast and accurate forecasting. It has better generalization. In this method, except considering main influential factors such as previous competitive load, system rotary reservation, competitive generating capacity etc, the past price data which are time series style or not have been included as attributes in input parameters. A day ahead MCP forecasting model is established. The results show that the proposed model has better forecasting accuracy in practical application.更多还原