基于SVM-GRNN的电力负荷建模的研究与应用  

Research and Application of Power Load Modeling Based on SVM-GRNN

作　　者：邓国良[1] DENG Guoliang(Laibin Power Supply Bureau of Guangxi Power Grid Corporation,Laibin Guangxi 546100)

机构地区：[1]广西电网公司来宾供电局,广西来宾546100

出　　处：《河南科技》2021年第22期110-112,共3页Henan Science and Technology

摘　　要：电力负荷预测是电网制定供电调度计划、实现经济调度的基础。广义回归神经网络和支持向量机是新型的智能算法。本文将这两种理论结合起来,建立组合模型。先通过支持向量机找到样本集中的最优中心,然后将该中心作为广义神经网络的径向基中心,对样本集进一步训练,并对某电力系统进行电力负荷建模。仿真预测结果表明,该模型收敛速度快,发挥了各算法的优点,预测结果精度高。Power load forecasting is the basis for power grid to formulate power supply dispatching plan and realize economic dispatching. Generalized regression neural network and support vector machine are new intelligent algorithms. This paper combines the two theories to establish a combined model. Firstly, the optimal center in the sample set is found by support vector machine, and then the center is used as the radial basis function center of generalized neural network, the sample set is further trained and the power load modeling of a power system is carried out. The simulation prediction results show that the model has fast convergence speed, gives full play to the respective advantages of the algorithm, and the prediction results have high accuracy.

关 键 词：负荷建模 支持向量机 广义回归神经网络 组合模型 

分 类 号：TM744[电气工程—电力系统及自动化]