基于灰色投影改进随机森林算法的电力系统短期负荷预测

针对短期负荷预测领域传统的机器学习算法(如人工神经网络、支持向量机等)存在的诸如泛化性能不强、参数和模型结构确定困难等问题,将随机森林回归算法引入短期负荷预测领域。同时应用投影原理改进了传统的灰色关联相似日选取算法,提出了一种基于灰色投影改进随机森林算法的电力系统短期负荷预测组合方法。基于灰色投影的相似日选取方法,采用灰色关联度判断矩阵表征历史样本与待预测日影响因素间的关联关系,并用熵权法确立影响因素的权重对判断矩阵加权,最后利用各个样本关联度投影值排序得到相似日集合。采用随机森林算法建立预测模型,利用灰色投影筛选出的相似日样本集合训练模型,最后输入预测日特征向量(天气预报数值、日类型等)完成预测。以浙江电网某县级市的负荷数据作为实际算例,并将上述方法与支持向量机方法以及未作灰色投影改进的随机森林算法进行对比。实验结果表明,新方法具有较高的预测精度和鲁棒性。

Power System Short-term Load Forecasting Based on Improved Random Forest with Grey Relation Projection

In view of the problems with typical machine learning algorithms (for example artificial neural network (ANN) and support vector machine (SVM)), such as the difficulty in determining the number of hidden units and parameter optimization, a random forest regression method is first introduced to power system load forecast. A new combinatorial algorithm involving two steps is proposed. Firstly, a grey relational judgment matrix is built to characterize the relationship between historical samples and forecasting sample. Secondly, the entropy method is used to determine the weights of all load influencing factors and the weighting matrix is got. Thirdly, the historical samples with bigger grey relation projection values are used to form the training set. After getting the training set, this data set is used to train random forest models. Then, the eigenvectors of the forecasting day are input to the trained model to finish the forecasting process. The real load data of one city in Zhejiang Province is used to test the proposed algorithm, and the results are compared with SVM and random forest method with no improvement made on grey relation projection. The results show that the new combinatorial method has higher precision and robustness than the other two methods. This work is supported by National Natural Science Foundation of China (No. 51277009).