【摘要】 为了提高电力系统短期负荷预测精度,针对传统径向基函数(RBF)神经网络在负荷预测中存在的问题,提出一种新的预测模型:粒子群优化的RBF神经网络模型。粒子群算法是一种新的全局优化算法,有很强的全局寻优能力,用它来优化RBF神经网络的权值,并用优化好的RBF网络进行负荷预测。仿真在虚拟仪器LabVIEW和Matlab软件平台上进行,结果表明该预测模型精度高于传统RBF神经网络模型,具有一定实用性。更多还原

【Abstract】 In order to improve the precision of short-term load forecasting,this paper proposes a new load forecasting model based on Particle Swarm Optimization(PSO).PSO is a novel random optimization method which has extensive capability of global optimization.PSO is used to optimize the weighting factor of Radial Basis Function(RBF)neural network and the optimal model is applied to forecast load.LabView and MATLAB are employed to implement the model for short-term load forecasting.The simulation results show that the load forecasting model optimized by PSO is more accurate than the traditional RBF model.更多还原