发电厂及电力系统专业的毕业论文VIP免费

大学毕业论文电力系统短期负荷预测姓名：学号：专业：发电厂及电力系统年级：指导教师：目录中文摘要:..............................................................1英文摘要:..............................................................21绪论..................................................................31.1短期负荷预测的目的和意义.......................................311.2电力系统负荷预测的特点和基本原理................................41.2.1电力负荷预测的特点........................................41.2.2电力负荷预测的基本原理....................................41.3国内外研究的现状...............................................51.3.1传统负荷预测方法.........................................61.3.2现代负荷预测方法.........................................61.4神经网络应用于短期负荷预报的现状...............................81.5本文的主要工作.................................................82最小二乘法...........................................................102.1最小二乘法原理................................................102.2多项式拟合具体算法............................................102.3多项式拟合的步骤...............................................112.4电力系统短期负荷预测误差......................................122.4.1误差产生的原因..........................................122.4.2误差表示和分析方法......................................122.4.3拟合精度分析............................................133基于神经网络的短期负荷预测...........................................153.1人工神经网络..................................................153.1.1人工神经网络的基本特点..................................153.2BP网络的原理、结构............................................153.2.1网络基本原理.............................................153.2.2BP神经网络的模型和结构..................................163.2.3BP网络的学习规则........................................163.3BP算法的数学描述..............................................173.3.1信息的正向传递...........................................173.3.2利用梯度下降法求权值变化及误差的反向传播................173.4BP网络学习具体步骤............................................183.5标准BP神经网络模型的建立.....................................193.5.1输入输出变量............................................193.5.2网络结构的确定..........................................193.5.3传输函数................................................203.5.4初始权值的选取..........................................213.5.5学习数率................................................223.5.6预测前、后数据的归一化处理..............................223.6附加动量的BP神经网络.........................................223.6.1标准BP算法的限制与不足.................................223.6.2附加动量法..............................................2324算例分析.............................................................254.1负荷数据......................................................254.1.114天实际的负荷数据......................................254.1.2归一化后的负荷数据......................................274.2两个模型仿真后的结果分析......................................304.3两种模型拟合精度分析..........................................374.4附加动量法....................................................39结论..................................................................40谢辞............