基于卡尔曼滤波的节假日短期负荷预测  

Short-term Holiday Load Forecasting Based on Kalman Filter

作　　者：王科[1] 陈丽华[1] 

机构地区：[1]西南交通大学,成都610031

出　　处：《电气技术》2014年第1期4-6,共3页Electrical Engineering

基　　金：中央高校基本科研业务费专项资金(SWJTU2011CX004EM)

摘　　要：随着电力系统智能化的不断发展,高精度的短期电力负荷预测成为电力系统经济运行研究的重要课题之一。在介绍了卡尔曼滤波原理的基础上,给出了卡尔曼滤波一步递推方程组。结合电力系统负荷情况,建立了卡尔曼滤波短期负荷预测模型,并对其进行预测。通过引入假期因子提出了改进的卡尔曼滤波算法,提高了卡尔曼滤波预测精度,验证了改进算法的正确性和有效性。With the continuous development of intelligent power system, high-precision short-term load forecasting becomes one of the important issues on the research of power system operation. Based on the introduction of the principle of Kalman filter, the Kalman filter step recursive equations are given. Combined power system load conditions, the Kalman filter short-time forecasting model of is established, follows its predictions. By introducing the holiday factor, the advanced algorithm is put forward. The prediction accuracy of the Kalman filter is improved, confirming the validity and effectiveness of the advanced algorithm.

关 键 词：短期假期负荷预测 卡尔曼滤波 假期因子 

分 类 号：TM715.1[电气工程—电力系统及自动化] TN713[电子电信—电路与系统]