基于波动类型精细划分与聚类的短期负荷预测

Short-term Load Forecasting Based on Fine Division and Clustering of Fluctuation Types

为减少短期负荷预测中负荷波动特性对负荷整体运行趋势的影响,提出一种面向波动类型精细划分与聚类的短期负荷组合预测方法。首先,引入k-means++将全年负荷按日特性聚类,并将聚类后的日负荷划分为负荷典型时段。其次,根据雨流计数法思想对负荷典型时段中的波动进行划分并结合模糊c-均值聚类算法(fuzzy c-means,FCM)以负荷波动特性为依据对负荷波动进行聚类。进一步,考虑到关键变量与负荷波动过程的关联关系,利用快速过滤特征选择算法(fast correlation-based filter,FCBF)将各负荷波动下对应的相关因素特征进行筛选。最后,建立以日负荷波动与负荷重构最优特征集为输入、以负荷功率为输出的短期负荷组合预测模型。实际算例表明,所提出的短期负荷组合预测方法能够显著提升短期负荷预测的精度。

In order to reduce the influence of load fluctuation characteristics on the overall operation trend of load in short-term load forecasting, a short-term load combination forecasting method oriented to fluctuation type fine division and clustering is proposed. Firstly, k-means++ is introduced to cluster the annual load according to its daily characteristics,and the clustered daily load is divided into typical load periods;Secondly, based on the idea of rain-flow counting method, the fluctuations in the typical load period are divided and combined with the fuzzy c-means clustering algorithm(FCM)to cluster the load fluctuations based on the characteristics of load fluctuations. Further, considering the relationship between the key variables and the process of load fluctuations, a fast correlation-based filter(FCBF) is applied to filter the characteristics of the corresponding correlation factors under each load fluctuation. Finally, a short-term load combination forecasting model with the daily load fluctuation and load reconstruction optimal feature set as the input and the load power as the output is established. Practical examples show that the proposed short-term load combination forecasting method can significantly improve the accuracy of short-term load forecasting.