基于卡尔曼滤波的短期负荷预测方法的研究

负荷历史数据是负荷预测的基础。负荷历史数据由于测量、人为等因素而造成不准确，因而导致负荷预测也不准确。文中提出利用最小二乘法线性拟合建立负荷数据基本模型，用3次样条插值对卡尔曼滤波器的系统参数进行辨识，最终用卡尔曼滤波器对历史数据进行预处理，以纠正由于测量错误或人为改动的数据。对文中所提方法进行了验证。结果表明，后验误差在3％之内，效果很好。

RESEARCH ON PROCESSING OF SHORT-TERM HISTORICAL DATA OF DAILY LOAD BASED ON KALMAN FILTER

The historical data of load is the basis of load forecasting, but the inaccuracy caused by the measurement and artificial factors will lead to the inaccuracy of load forecasting inevitably. Thus, it is indispensable in load forecasting to process the historical data of load. A method to establish basic model of load data by least square linear fitting is presented in which the system parameters of Kalman filter are identified by cubic spline interpolation, then the pre-processing of historical data is performed by Kalman filter to correct the bad data caused by measurement error or human made alteration. After the processing of pseudo-data in historical data of load from practical power system, the posterior error of load forecasting is within 3%.