基于云计算的电网负荷预测

负荷预测是电网管理的重要组成部分。准确及时的负荷预测对于制定经济合理的配电方案，提高电网运行的安全性和经济性，提高电能质量具有重要意义。为此，提出了一种基于人工鱼群和基因表达式编程的电力负荷预测函数挖掘算法。在此基础上，还提出了基于云计算的分布式负荷预测模型挖掘来解决大规模电力负荷预测问题。为了更好地提高模型的预测精度，在分布式负荷预测模型中引入了误差最小化交叉。实验结果表明，提出的算法在平均时间消耗、平均收敛数、预测精度以及并行计算性能方面具有优势。

Load forecasting of power grid based cloud computing

Load forecasting is an important part of grid management.Accurate and timely load forecasting is of great significance for the formulation of economic and reasonable power distribution solutions，improving the safety and economy of power grid operation，and improving power quality.Therefore，this paper proposes an electric load forecasting function mining algorithm based on artificial fish school and gene expression programming.On this basis，also proposes a distributed load forecasting model mining based on cloud computing to solve large-scale power load forecasting problems.In order to improve the prediction accuracy of the model，the error minimization intersection is introduced in the distributed load forecasting model.The experimental results showed that the proposed algorithm had advantages in average time consumption，average convergence，prediction accuracy and parallel computing performance.