基于卡尔曼滤波二次修正的短期负荷预测

针对卡尔曼滤波在短期负荷预测中只是进行一步预测的问题，提出了由预测协方差阵构建测量方差方程的方法，对测量新息做出估计，实现了一步预测基础上的二次修正。给定某一置信度，得出负荷相应置信水平下的置信区间包络线，以此为风险分析、可靠性评估提供数据支持，对修正结果进行了确认。通过对实际电网1周的负荷数据进行仿真分析，并与普通卡尔曼滤波及基于移动窗的滤波算法分别进行比较，验证了提出方法的有效性和优越性。

Short-Term Load Forecasting Based on Second-Order Correction of Kalman Filter

For short-term loads, the Kalman filtering theory only makes one-step-ahead forecasts. This paper proposes a method based on a second-order correction to improve the onestep-ahead forecasting. It first uses the estimated covariance matrix to calculate the variance of forecasted value, and then uses the standard error of the calculated variance to correct the forecasted value. Given a confidence level, the envelope of confidence interval, which is the data support of risk analysis and reliability assessment, is estimated. The envelope also helps to construct the final corrected forecast. The proposed method is compared with the basic Kalman filter and Kahnan filtering algorithm with moving windows using electricity load data during one week from a local electricity network. Simulation results show that the proposed method is effective and performs better than the other two methods.