| L-M优化BP算法在短期负荷预测中的应用 |
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| 引用本文： | 代小红,王光利.L-M优化BP算法在短期负荷预测中的应用[J].计算机科学,2011,38(7):265-267. |
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| 作者姓名： | 代小红 王光利 |
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| 作者单位： | 1. 重庆工商大学计算机科学与信息工程学院,重庆,400067
2. 重庆邮电大学生物信息学院,重庆,400065 |
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| 基金项目： | 本文受重庆市教委科学技术研究项目(KJ090728)资助。 |
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| 摘 要： | 在分析传统BP算法的不足的基础上，提出了将Levenbery-Marquard、优化法与神经网络模型相结合的L-M优化BP算法。此方法与传统算法相比学习速度得到了提高，网络的收敛加快，尽量避免了系统陷入局部最小;针对某电力局某地区的单条线路的实际数据，采用基于Levenbery-Marquardt优化的I3P算法的神经网络模型对其进行了仿真，结果表明该方法具有较高的预测精度和较强的适应能力。
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| 关 键 词： | 短期负荷预侧，L-M优化法，BP算法，预测误差 |
Application of L-M Optimized BP Algorithm in Short-term Power Load Forecast |
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| DAI Xiao-hong,WANG Guang-li.Application of L-M Optimized BP Algorithm in Short-term Power Load Forecast[J].Computer Science,2011,38(7):265-267. |
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| Authors: | DAI Xiao-hong WANG Guang-li |
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| Affiliation: | (College of Computer and Information Engineering,Chongqing Technology and Business University,Chongqing 400067,China)
(School of Bioinlormation,Chongqing University of Post and Telecommunications,Chongqing 400065,China) |
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| Abstract: | Analyzing the deficiency of the traditional BP algorithm, combining Levenbery-Marquardt optimized algorithm and a neural network forecasting method, this paper put forward a L-M optimized BP algorithm, which quickens the train, improves stability and avoids trapping into local minimum. For some area power supply load of Power Corporation in somewhere, a short term load forecast was simulated based on L-M optimized BP algorithm. Analyzing the simulation results, it shows the L-M optimized BP algorithm has better forecast precision and adaptive capacity. |
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| Keywords: | Short term load forecasting L-M optimized BP algorithm Forecast precision |
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