| 智能电网中电力负荷短期预测数据挖掘模型 |
| |
| 引用本文： | 高庆敏,孟繁为,王利平,蔡宇飞. 智能电网中电力负荷短期预测数据挖掘模型[J]. 华北水利水电学院学报, 2011, 32(3): 43-45 |
| |
| 作者姓名： | 高庆敏 孟繁为 王利平 蔡宇飞 |
| |
| 作者单位： | 华北水利水电学院,河南郑州,450011 |
| |
| 摘 要： | 依据数据挖掘理论对数据进行收集、整合,运用改进型BP神经网络模型处理数据,建立电力负荷模型进行短期预测.通过不同精度下的实验分析,结果表明,改进型神经网络负荷预测模型在高精度下预测结果优于低精度下预测结果,最大误差同比降低80%,适用实际负荷预测.
|
| 关 键 词： | 数据挖掘 改进BP算法 人工神经网络 电力负荷预测 L-M法 |
Electrical Load Forecast of Data Mining Model in Smart Grid |
| |
| GAO Qing-min,MENG Fan-wei,WANG Li-ping,CAI Yu-fei. Electrical Load Forecast of Data Mining Model in Smart Grid[J]. Journal of North China Institute of Water Conservancy and Hydroelectric Power, 2011, 32(3): 43-45 |
| |
| Authors: | GAO Qing-min MENG Fan-wei WANG Li-ping CAI Yu-fei |
| |
| Affiliation: | (North China Institute of Water Conservancy and Hydroelectric Power,Zhengzhou 450011,China) |
| |
| Abstract: | Based on data mining theory to collect and integrate data,using improved BP neu ral network model to process the grid data,and a electrical load model was esta blished to forcast the short-term electrical load.Through different precision experiments,the high-precision forcast result is better than the low-precisio n forcast result,the maximum error is reduced by 80%,it is proved that the mod el suits to the practical. |
| |
| Keywords: | data mining modified BP algorithm Artificial Neural Networks electrical load forecast Levernberg-Marquardt algorithm. |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ |
|
