摘要
根据电力负荷的主要影响因素,考虑时间和天气,建立了基于粒子群算法(PSO)和径向基函数(RBF)神经网络的短期负荷预测模型.由粒子群算法对RBF神经网络的训练进行优化,提高了模型的可信度和可靠性.结果表明,该方法具有较高的预测精度,有一定的应用前景.
With the main influential factors on electric power load, the weekday and weather considered, a load forecasting model based on PSO（Particle Swarm Optimizers） and RBF（Radial Basis Function） is constructed. PSO is utilized to optimize the RBFNN training process, which has improved the credibility and reliability of model. The result indicates that this method has high predicting precision and a prospect for application.
基金
安徽省教育厅自然科学基金资助项目(2006kj031b)
