基于熵权法与GRA-ELM的配电网空间负荷预测

空间负荷预测对配电网规划建设具有重要意义。为了提高配电网空间负荷预测精度,文中提出基于熵权法与灰色关联分析-极限学习机(GRA-ELM)的配电网空间负荷预测方法。首先,将规划区域内的小区按用地性质划分,分析不同类型负荷的影响因素,建立空间负荷密度指标体系;其次,利用熵权法对不同类型负荷的负荷密度指标进行权重分配;然后,应用GRA挑选出与待测地块负荷密度指标相似的训练样本;最后,将样本带入经粒子群优化(PSO)算法参数处理后的极限学习机(ELM)进行训练,得到预测结果。通过实例对所提方法的性能进行仿真验证,结果表明,所提方法相对其他方法的空间负荷预测精度更高。

Spatial load forecasting of distribution network based on entropy weight method and GRA-ELM

Spatial load forecasting is of great significance for distribution network planning and construction. In order to improve the accuracy of distribution network spatial load forecasting, a distribution network spatial load forecasting method based on entropy weight method and grey relational analysis-extreme learning machine (GRA-ELM) is proposed. Firstly, the districts in the planning area are seperated according to the nature of land use. The influencing factors of different types of load are analyzed, and the index system of spatial load density is established. Secondly, the entropy weight method is used to distribute the weight of load density index for different types of load. Then, GRA is used to select the training samples which are similar to the load density index of the plot to be tested. Finally, the samples are brought into the extreme learning machine (ELM) model after the parameters are optimized by particle swarm optimization (PSO), and the prediction results are obtained. The performance of the proposed method is verified by an example and the results show that the proposed spatial load forecasting method has high accuracy than other methods does.