| 基于PSO的RBF神经网络短期电力负荷预测 |
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| 引用本文： | 田丽,黄世伟,李泽应,王军.基于PSO的RBF神经网络短期电力负荷预测[J].安徽工程科技学院学报,2007,22(2):33-35. |
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| 作者姓名： | 田丽 黄世伟 李泽应 王军 |
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| 作者单位： | 安徽工程科技学院,安徽省电气传动与控制重点实验室,安徽,芜湖,241000 |
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| 基金项目： | 安徽省教育厅自然科学基金资助项目(2006kj031b) |
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| 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.
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| 关 键 词： | 径向基函数 粒子群 短期负荷 预测 |
| 文章编号： | 1672-2477（2007）02-0033-03 |
| 修稿时间： | 2006-12-22 |
A combined model of PSO algorithm and RBF neural network for short-term load forecasting |
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| TIAN Li,HUANG Shi-wei,LI Ze-ying,Wang Jun.A combined model of PSO algorithm and RBF neural network for short-term load forecasting[J].Journal of Anhui University of Technology and Science,2007,22(2):33-35. |
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| Authors: | TIAN Li HUANG Shi-wei LI Ze-ying Wang Jun |
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| Institution: | Anhui Prov. Key Lab. of Elec. and Contr. ,Anhui University of Technology and Science, Wuhu 241000, China |
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| Abstract: | 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. |
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| Keywords: | RBFNN PSO short-term load forecasting |
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