摘要
根据电力负荷的主要影响因素,考虑了休息日和气候因素的影响,建立了基于粒子群算法(PSO)的级联网络短期负荷预测模型.通过粒子群算法对级联网络的训练进行优化,提高模型的运算速度.结果表明,该方法预测精度较高,效果较好.
The main effect factors for electric power load, along with the effects of weekend and weather, are considered. A Cascade neural network model for load forecasting based on PSO（Particle Swarm Optimizers） is constructed. PSO is utilized to optimize the RBFNN training process, which has improved the training speed of the model. The result indicates that this method has favorably high predicting precision.
基金
安徽省教育厅自然科学基金资助项目(2006kj031b)
关键词
级联网络
粒子群
预测
短期负荷
cascade neural network （CNN）
particle swarm optimizers （PSO）
forecasting
short-term load
