| 基于PSO改进的T-S模糊神经网络短期电力负荷预测研究 |
| |
| 引用本文： | 沈娟,刘大明.基于PSO改进的T-S模糊神经网络短期电力负荷预测研究[J].上海电力学院学报,2017,33(4):325-330. |
| |
| 作者姓名： | 沈娟 刘大明 |
| |
| 作者单位： | 上海电力学院 自动化工程学院,上海电力学院 计算机科学与技术学院 |
| |
| 基金项目： | 大型电气传动系统与装备技术国家重点实验室开放基金课题（SKLLDJ032016021）. |
| |
| 摘 要： | 精确的电力负荷预测有利于保障电网运行的安全性、稳定性、高效性及经济性.为提高预测精度,采用了一种PSO改进T-S(Takagi-Sugeno)模糊神经网络方法.分析了数据预处理对改善输入量的重要性,讨论了可以让学习率和平滑因子动态调节的改进T-S模糊神经网络算法,从而使PSO找到最优参数,然后结合历史负荷数据、相关影响因素进行预测,以表明改进的T-S模糊神经网络在短期电力负荷中具有更高的控制精度.
|
| 关 键 词： | 预测精度 PSO优化T-S算法 模糊模型 异常数据 |
| 收稿时间： | 2017/3/9 0:00:00 |
Review of Short-Term Power Load Forecasting Based on PSO Improved TS Fuzzy-Neural Network |
| |
| SHEN Juan and LIU Daming.Review of Short-Term Power Load Forecasting Based on PSO Improved TS Fuzzy-Neural Network[J].Journal of Shanghai University of Electric Power,2017,33(4):325-330. |
| |
| Authors: | SHEN Juan and LIU Daming |
| |
| Affiliation: | School of Automation Engineering, Shanghai University of Electric Power, Shanghai 200090, China and School of Computer Science and Technology, Shanghai University of Electric Power, Shanghai 200090, China |
| |
| Abstract: | Accurate power load forecasting is beneficial to ensure the safety,stability,efficiency and economy of grid operation.In order to improve the prediction accuracy,a PSO improved T-S (Takagi-Sugeno) fuzzy neural network method is applied.The importance of data preprocessing to improving the input is analyzed and an improved T-S fuzzy neural network algorithm that can make the learning rate and smoothing factor adjust dynamically is discussed.Therefore,it can make PSO find the optimal parameters.And then,combined with historical load data and related influencing factors,it is concluded that the improved T-S fuzzy neural network has higher control precision in short-term power load. |
| |
| Keywords: | prediction accuracy PSO-improved T-S algorithm fuzzy model abnormal data |
| 本文献已被 CNKI 等数据库收录！ |
| 点击此处可从《上海电力学院学报》浏览原始摘要信息 |
|
点击此处可从《上海电力学院学报》下载全文 |
