基于即时学习算法的短期负荷预测方法

针对电力系统短期负荷数据存在非线性和时变性等问题,提出了一种变量相关性局部即时学习算法和最小二乘支持向量机(LSSVM)的电力系统短期负荷预测模型。利用互信息计算气象数据、各气象因素等变量的相关度,并引入到即时学习算法训练集中,用以选择当前电力系统负荷的建模邻域,提高系统短期负荷模型预测的精度。利用相似度阈值对局部模型进行自适应更新,增强系统负荷模型实时性。利用Matlab对某市宛城区的负荷量进行预测,结果表明,基于即时学习算法的电力系统短期负荷预测模型误差更小,系统预测精度更高。

Forecasting of short-term power based on just-in-time learning

In view of the non-linearity and time-varying of short-term load data in power system, a local instant learning algorithm based on variable correlation and a short-term load forecasting model based on least squares support vector machine are proposed. Mutual information is used to calculate the correlation of meteorological data, meteorological factors and other variables, and it is introduced into the training set of real-time learning algorithm to select the current power system load modeling neighborhood and improve the accuracy of short-term load model prediction. Similarity threshold is used to update the local model adaptively to enhance the real-time performance of the system load model. The load forecasting in Wancheng District of a City is carried out by using Matlab. The results show that the short-term load forecasting model based on instant learning algorithm has smaller error and higher prediction accuracy. This work is supported by National Natural Science Foundation of China (No. 71471060) and Scientific and Technological Project of Henan Province (No. 202102210134).