基于模糊聚类与随机森林的短期负荷预测

摘要

针对传统数据挖掘算法(神经网络和支持向量机)进行短期负荷预测容易陷入局部最优,模型难以确定等问题,提出一种模糊聚类技术与随机森林回归算法结合的短期负荷预测方法.基于模糊聚类技术选取相似日的方法,考虑负荷的周期性变化特征,利用样本输入进行样本聚类,选取同类数据作训练样本,建立随机森林负荷预测模型.实例中负荷数据采用安徽省某地的历史负荷,用上述方法对该地区的日24小时负荷进行预测,并与传统的支持向量机和BP神经网络方法进行比较,验证了该方法的有效性.%Typical data mining methods (ANN and SVM) are applied in short-term load forecasting widely.However,these methods have some deficiencies including being trapped in local optimization easily and ensure the model hardly and so on.In order to overcome shortcomings,a method of combination of fuzzy clustering and random forest (RF) for load forecasting is proposed in this paper.On the other hand,various features of the periodical load and the similarity of input samples are considered in the proposed method.Input samples are clustered depending on similarity.Then,load forecasting model is established based on random forest algorithm and similar data are selected as training samples.The final results rely on the historical loads in Anhui for hourly load forecasting.And the results show that the proposed method is better than traditional support vector machine and BP neural network.