电力负荷预测毕业论文中英文资料外文翻译文献(4)

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s?????k??x????1????x????n??x????1?? ??x0000k?2n?1 s?s?1?????????n??x???n?? xk?x1?x????????20000k?2n?1根据上述公式，GM（1，1）-IGA的指标的校验值见表1。

表1 GM-IGA和GM的四个指标 平均相对误差 均方差率 小误差概率 关联度

通过表1可以看出，GM-GA所以指标的精确度都是一级的，因此这个GM（1，1）-IGA可以被用来预测短期负荷。

第四步：在图1中，我们可以得到GM（1，1）-IGA的预测负荷数据曲线比GM（1，1）的曲线更接近于原始的日负荷数据曲线。进一步分析，本文选择相对误差作为标准来评价两种模式。两种模型的偏差值如下，GM（1，1）的平均误差为2.285%，然而，GM（1，1）-IGA的平均误差为0.914%。

GM-GA 0.000090 0.0039 1 0.98 GM 0.0001 0.0073 0.92 0.90

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第五章．结论

本文提出了GM（1，1）-关于改进的遗传算法（GM（1，1）-IGA）来进行短期负荷预测。采用十进制编码代表性方案，改进的遗传算法用于获得GM（1，1）模型中?的最优值。本文也提出了单点线性算术交叉法，它能极大地提高交叉和变异的速度，因此GM（1，1）-IGA可以准确地预测短期日负荷。GM（1，1）-IGA的特点是简单、易于开发，因此，它在电力系统中作为一个辅助工具来解决预测问题是适宜的。

图2.GM（1，1）的偏差值

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图3.GM（1，1）-IGA的偏差值

致谢

这项工作是由国家自然科学基金部分支持。（70671039）

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Application of Improved Grey Prediction Model

for Power Load Forecasting

[Abstract] Although the grey forecasting model has been successfully utilized in many fields, literatures show its performance still could be improved. For this purpose, this paper put forward a GM (1, 1)-connection improved genetic algorithm (GM (1, 1)-IGA) for short-term load forecasting (STLF). While Traditional GM (1,1) forecasting model is not accurate and the value of parameter ? is constant, in order to solve this problem and enhance the accuracy of short-term load forecasting (STLF), the improved decimal-code genetic algorithm (GA) is applied to search the optimal ? value of grey model GM (1, 1). What’s more, this paper also proposes the one-point linearity arithmetical crossover,which can greatly improve the speed of crossover and mutation. Finally, a daily load forecasting example is used to test the GM (1, 1)-IGA model and traditional GM (1, 1) model, results show that the GM (1, 1)-IGA had better accuracy and practicality.

Keywords: Short-term Load Forecasting, Grey System,Genetic Algorithm, One-point Linearity Arithmetical Crossover.

1. Introduction

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