基于相关向量机的电力负荷中期预测

针对电力负荷中期预测比较困难并且存在较大误差的问题,提出了一种基于相关向量机的中期预测方法.结合EUNITE网络提供的实际数据,研究了日最大负荷前后期关系、日最大负荷与节假日的关系和当日与对应星期数的相关性,并建立了相应的电力负荷中期预测模型.该模型是将与某天相关的n个前期信息作为该天的日最大负荷的输入量,而日最大负荷与节假日、当日(星期数)的关系信息用两个二元值表示.在模型训练前,将输入量的前7个属性值和预测目标值进行归一化处理 采用不同训练样本集的仿真实验结果表明,相关向量机方法比支持向量机方法具有更多的优点,当高斯核函数的宽度值取为2 0时,相关向量机方法具有较为理想的预测效果.

Mid-Term Electric Load Prediction Based on the Relevant Vector Machine

The mid-term electric load prediction is an existing difficult problem whose predicted solution (often) has a larger error. An RVM (relevant vector machine)-based mid-term prediction method is proposed for solving the problem. With the practical data provided by EUNITE-network, the relations of before-after duration of daily maximum load, the relation between daily maximum load and holidays, and existing relation between the day's maximum load and the corresponding weeks are investigated and the model of the mid-term electric load prediction is constructed. In the model, the .n. preceding information related to certain day is regarded as daily maximum load of that day, and the relation information between the daily maximum load and holidays, number of weeks (the day ) is represented as two bi-values. Before training the model, the preceding 7 attribute values of input variables and the predictive goal value are normalized. Using different train sample sets, the simulation experiment result demonstrates that the RVM method has more advantages than support vector machine method. When the Gaussian kernel function width is 2.0, RVM method possesses more perfect prediction performance.