【作者】 芦森；

【导师】 龚灏； 周仲礼；

【作者基本信息】 成都理工大学 ， 计算数学， 2010， 硕士

【摘要】 能源是人类生存、经济发展、社会进步和现代文明不可缺少的重要物质基础。随着社会经济的不断发展,能源需求也在不断增长。因此对能源需求的研究具有重要的理论意义和现实意义。能源需求预测是从研究一个国家、地区或特定范围内能源消费的历史与现状开始,根据其消费行为,归纳影响能源消费的各种因素,寻求消费与这些因素的关系,根据这些关系对未来能源需求发展趋势作出估计和评价。因此对能源需求进行建模与预测是制订能源发展战略、规划部署的基础之一。近年来,很多学者对能源需求的预测进行了研究,有很多的预测方法。本文将经济增长、产业结构、能源消费结构、人口和城市化、居民消费水平、技术进步和环境政策等作为对能源需求的影响因素。利用1978~2008年的能源需求总量时间序列数据,通过时间序列、灰色理论以及BP神经网络分别对2010-2015中国能源需求总量进行了预测。组合预测模型比单个预测模型具有更高的预测精度,能增强预测的稳定性,具有较高的适应未来预测环境变化的能力,本文以最小预测误差平方和为目标函数的线性组合预测模型,计算出时间序列、灰色理论以及BP神经网络组合模型的权重系数,利用1978~2008年的数据进行建模和检验,结果表明组合模型预测结果的平均相对误差为2.19%,比时间序列、灰色理论以及BP神经网络的平均相对误差分别小1.93%、2.14%以及1.12%,利用此组合模型预测到2015年我国能源需求总量将达到385781万吨标准煤。更多还原

【Abstract】 Energy is an indispensable material basis of human survival, economic development, social progress and modern civilization,With the development of social economy, energy demand is also growing.Therefore, the demand for energy research has important theoretical and practical significance. Energy demand forecast is from a country, region or specific energy consumption‘s past and present . According to their consumption behavior, summarized the various factors affecting energy consumption, for consumption and the relationship between these factors. Therefore, the demand for energy modeling and forecasting is one of the foundations to develop energy development strategy, planning the deployment.In recent years, many scholars has been studied the energy demand forecast, there are many prediction methods. The article take the economic growth, industrial structure, energy consumption, population and urbanization, the level of consumption, technological progress and environmental policies as energy demand factors. Use the total energy demand time-series data from 1978 to 2008, through the time series, gray theory, and BP neural network respectively, forecasted the total energy demand of China from 2010 to 2015.Combination forecasting model has higher prediction accuracy than a single prediction model ,it can enhance the stability of prediction, and the higher forecast to adapt to future environmental change, In this paper, the minimum sum of squares prediction error as the objective function of the linear combination forecast model Calculated the weight factor of the time series, gray theory, and BP Neural Network Model, Using data from 1978 to 2008 to build and test model. The results show that the the average relative error of combined model forecasted is 2.19%,which is 1.93% smaller than time series, 2.14% smaller than gray theory and 1.12% smaller than BP neural network. The combined model forecasted that by 2015 China’s total energy demand will reach 3,857,810,000 tons of standard coal.更多还原