Considering the factors affecting the increasing rate of power consumption, the BP neural network structure and the neural network forecasting model of the increasing rate of power consumption were established. Immune...Considering the factors affecting the increasing rate of power consumption, the BP neural network structure and the neural network forecasting model of the increasing rate of power consumption were established. Immune genetic algorithm was applied to optimizing the weight from input layer to hidden layer, from hidden layer to output layer, and the threshold value of neuron nodes in hidden and output layers. Finally, training the related data of the increasing rate of power consumption from 1980 to 2000 in China, a nonlinear network model between the increasing rate of power consumption and influencing factors was obtained. The model was adopted to forecasting the increasing rate of power consumption from 2001 to 2005, and the average absolute error ratio of forecasting results is 13.521 8%. Compared with the ordinary neural network optimized by genetic algorithm, the results show that this method has better forecasting accuracy and stability for forecasting the increasing rate of power consumption.展开更多
文章结合数据仓库、决策支持系统、数据挖掘预测算法等理论知识,用以高校后勤水电管理、和电力企业的用电量分析预测为主题,采用MS SQL Server 2005的集成BI开发平台进行数据仓库多维数据集设计开发,并利用曲线拟合(非齐次指数模型)预...文章结合数据仓库、决策支持系统、数据挖掘预测算法等理论知识,用以高校后勤水电管理、和电力企业的用电量分析预测为主题,采用MS SQL Server 2005的集成BI开发平台进行数据仓库多维数据集设计开发,并利用曲线拟合(非齐次指数模型)预测算法,对未来中长期的用电量进行预测,实现了集报表分析、用电量预测等多种功能为一体的较为完善的用电营销决策支持系统。展开更多
基金Project(70373017) supported by the National Natural Science Foundation of China
文摘Considering the factors affecting the increasing rate of power consumption, the BP neural network structure and the neural network forecasting model of the increasing rate of power consumption were established. Immune genetic algorithm was applied to optimizing the weight from input layer to hidden layer, from hidden layer to output layer, and the threshold value of neuron nodes in hidden and output layers. Finally, training the related data of the increasing rate of power consumption from 1980 to 2000 in China, a nonlinear network model between the increasing rate of power consumption and influencing factors was obtained. The model was adopted to forecasting the increasing rate of power consumption from 2001 to 2005, and the average absolute error ratio of forecasting results is 13.521 8%. Compared with the ordinary neural network optimized by genetic algorithm, the results show that this method has better forecasting accuracy and stability for forecasting the increasing rate of power consumption.
文摘文章结合数据仓库、决策支持系统、数据挖掘预测算法等理论知识,用以高校后勤水电管理、和电力企业的用电量分析预测为主题,采用MS SQL Server 2005的集成BI开发平台进行数据仓库多维数据集设计开发,并利用曲线拟合(非齐次指数模型)预测算法,对未来中长期的用电量进行预测,实现了集报表分析、用电量预测等多种功能为一体的较为完善的用电营销决策支持系统。
