| 基于粒子群算法的短期电力负荷预测 |
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| 引用本文： | 田丽,黄鹏程,黄世伟,王军,李泽应.基于粒子群算法的短期电力负荷预测[J].安徽工程科技学院学报,2007,22(3):51-53. |
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| 作者姓名： | 田丽 黄鹏程 黄世伟 王军 李泽应 |
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| 作者单位： | 1. 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
2. 安徽万德福电子有限公司,安徽,旌德,242600 |
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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.
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| 关 键 词： | 级联网络 粒子群 预测 短期负荷 |
| 文章编号： | 1672-2477（2007）03-0051-03 |
| 修稿时间： | 2007-04-09 |
Short-term load forecasting based on PSO algorithm |
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| TIAN Li,HUANG Peng-cheng,HUANG Shi-wei,WANG Jun,LI Ze-ying.Short-term load forecasting based on PSO algorithm[J].Journal of Anhui University of Technology and Science,2007,22(3):51-53. |
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| Authors: | TIAN Li HUANG Peng-cheng HUANG Shi-wei WANG Jun LI Ze-ying |
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| Institution: | 1. Anhui Provincial Key Laboratory of Electric and Control, Anhui University of Technology and Science, Wuhu 241000, China; 2. Anhui Wonderful Electron Co. Ltd. ,Jinde 242600,China |
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| Abstract: | 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. |
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| Keywords: | cascade neural network(CNN) particle swarm optimizers(PSO) forecasting short-term load |
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