基于支持向量回归和K均值聚类的降温负荷组合测算模型

Combined Cooling Load Estimation Model Based on Support Vector Regression and K-means Clustering

随着降温负荷在负荷结构中占比逐年增大,测算降温负荷对中短期负荷预测意义重大。受经济新常态、去产能等政策影响,基本负荷在月间出现较大差异,传统降温负荷测算方法对该类情况有局限性。构建了一种基于支持向量回归和K均值聚类的降温负荷组合测算模型,包括基于SVR-Winters的变尺度基本负荷预测和EMD-Kmeans降温负荷二次剥离。以西北某省实际数据进行算例分析,结果表明该方法能有效解决基本负荷月间差异较大及日内随机波动等问题,具有较高的测算精度及良好适应性。

With the increasing proportion of cooling load in load structure year by year,it is of great significance to estimate the cooling load for the load medium and short term load forecasting.Influenced by the new normal economy and capacity elimination policies,the basic load varies greatly from month to month,and traditional cooling load calculation methods will be no longer applicable.This paper constructs a combined cooling load estimation model based on support vector regression and K-means clustering,including SVR-Winters-based variable-scale basic load forecasting and secondary stripping of EMD-Kmeans cooling load.Based on the actual data of a certain province in Northwest China,the results show that the method can effectively solve the problems such as large monthly differences in the basic load and intra-day random fluctuations,and has higher accuracy and better adaptability.