大数据技术在短期负荷预测的应用

Application of Big Data Technology in Short Term Load Forecasting

随着电网的智能化升级其处理数据的能力越来越强,这为电力系统负荷的预测开辟了一条新的道路。将支持向量机技术同大数据技术结合在一起,建立负荷非线性回归方程,然后利用Hadoop处理框架的大数据技术解决方法 ,此类方法结合了支持向量机技术对非线性数据的处理能力和大数据的数据挖掘能力的优点,结果表明该算法同传统的算法相比,有更好的精确性。本文将支持向量机技术同大数据技术结合在一起,建立负荷非线性回归方程,然后利用Hadoop处理框架的大数据技术解决方法 ,此类方法结合了支持向量机技术对非线性数据的处理能力和大数据的数据挖掘能力的优点,结果表明该算法同传统的算法相比,有更好的精确性。

With the intelligent upgrading of the power grid,its ability to process data is becoming more and more powerful,which opens a new way for the load forecasting of the power system.Combining the support vector machine technology with big data technologies to establish load nonlinear regression equation,and then using a large data processing framework Hadoop technology solutions,this method combines the advantages of processing ability of support vector machine technology for nonlinear data and large data mining capabilities,the results show that the algorithm compared with traditional algorithm and have better accuracy.This paper combines the support vector machine technology with big data technologies to establish load nonlinear regression equation,and then using a large data processing framework Hadoop technology solutions,this method combines the advantages of processing ability of support vector machine technology for nonlinear data and large data mining capabilities,the results show that the algorithm compared with the traditional the algorithm has a better accuracy.