基于GRA-LSSVM密度法的配电网空间负荷预测方法研究

为提高配电网的规划水平，实现配电网的合理规划与改造，以有效提高供电质量和效益。针对配电网空间负荷预测，设计了一种新型的电网负荷密度预测算法，在算法中将支持向量机引入到基于灰色关联度分析的负荷预测模型。通过灰色关联度分析法筛选出更符合要求的样本并进行训练，同时，引入了混沌粒子群算法(PSO)对此模型进行优化，以提高算法的精度。通过实际数据对这种算法的性能进行实例分析，依据分析结果表明，提出的算法与其他方法相比对配电网空间负荷预测的精度有显著差异，本文方法可以有效的提高配电网负荷密度预测的精度。

Research on spatial load forecasting of distribution network based on GRA-LSSVM density method

In order to improve the planning level of distribution network and realize the rational planning and transformation of distribution network, the quality and efficiency of power supply can be improved effectively. A new load forecasting model is proposed for the distribution network load forecasting. In this algorithm, the support vector machine (SVM) is introduced into the load forecasting model based on grey relational analysis. The grey correlation analysis method is used to select the more suitable samples and train them. At the same time, the chaotic particle swarm optimization (PSO) is introduced to optimize the model to improve the accuracy of the algorithm. For example through the analysis of actual data on the performance of the algorithm, based on the results of the analysis show that there are significant differences in the algorithm proposed and using this method to predict the space distribution load precision. The method proposed can improve the distribution network load density prediction effective accuracy.