山东省电力需求预测及其与经济发展关系研究

发布时间：2019-04-22 14:55

【摘要】：电力是国民经济发展的基础性能源，在整个能源体系中占据着至关重要的地位，与国民经济的各行各业都息息相关，对国民经济的发展具有关键性的支撑作用。电力行业的健康发展能为经济发展提供强大的能源支撑，促进经济发展；反之，经济的持续发展又能推动电力产业的发展。电力的短缺则会对经济的持续健康发展产生严重的制约作用。由于电力不同于其他能源，不能储存并且不存在库存，能够比较真实的反应社会经济的运行状况，因而，经济发展对电力的需求和电力的供应往往成为社会经济发展的风向标。本文采用离散二阶差分方法，对山东2020年电力需求进行预测。根据离散二阶差分方程的预测结果，山东省2020年电力需求达到5906.64亿千瓦时，相对于2012年上涨了55.66%，上升的幅度看似不大，但是由于电力需求的基数较大，增长的绝对数量值为2112.06，相当于2006年山东全省的电力消费量。因此，山东电力需求在未来几年面临一定的压力。随后利用向量自回归模型，对山东电力需求与经济发展的关系进行分析，由脉冲响应函数分析可知，山东省经济发展随着用电的增长，在经过一定快速增长期之后，由于规模效应、边际效应的原因，在电力需求增加的情况下，经济增长的速度有所减缓。山东经济的增长需要电力能源的支持，并且经济发展越快，需要电力能源的支持力度越大。不过在经济发展到一定程度之后，随着新能源的开发与能源利用效率的提高，经济的发展对电能的依赖程度会变小，加之第三产业等低能耗的行业高速发展，因此会出现山东经济发展与电力需求负相关的现象。最后应用VAR模型对离散二阶差分方法的规律性进行检验，由整合数据序列所构建的VAR模型，，从时间序列数据的平稳性、两组整合数据的协整关系与VAR模型的各项指标数据可知，离散二阶差分方法预测出的数据在规律性上具有高精准度。由离散二阶差分方法的数值误差率分析可知，离散二阶差分方法所预测出的数据误差率整体较低，体现出较高的精准度。因此，本文采用离散二阶差分方法所预测出的山东电力需求数据具有很高的可信度，对山东未来电力产业的发展提供数据支持与参考。
[Abstract]:Electric power is the basic energy for the development of national economy, which occupies an important position in the whole energy system. It is closely related to all kinds of industries of the national economy and plays a key supporting role in the development of the national economy. The healthy development of the electric power industry can provide powerful energy support for the economic development and promote the economic development, on the other hand, the sustainable development of the economy can promote the development of the electric power industry. The shortage of electricity will seriously restrict the sustainable and healthy development of economy. Because electricity is different from other energy sources, it can not be stored and there is no inventory, so it can reflect the operation of social economy more realistically. Therefore, the demand for electricity and the supply of electricity in economic development often become the vane of socio-economic development. In this paper, the discrete second-order difference method is used to forecast the power demand of Shandong Province in 2020. According to the forecast results of the discrete second-order difference equation, Shandong Province's electricity demand reached five hundred and ninety billion six hundred and sixty four million kilowatt-hours in 2020, up 55.66% compared to 2012, and the increase appears to be small, but due to the large base of electricity demand, The absolute value of the increase is 2112.06, equivalent to the electricity consumption of Shandong Province in 2006. Therefore, Shandong electric power demand faces certain pressure in the next few years. Then the relationship between power demand and economic development in Shandong Province is analyzed by using vector autoregressive model. According to the analysis of impulse response function, the economic development of Shandong Province with the increase of electricity consumption, after a certain period of rapid growth, after a certain period of rapid growth, Because of scale effect and marginal effect, the speed of economic growth slows down with the increase of electricity demand. The economic growth of Shandong needs the support of electric energy, and the faster the development of economy, the greater the support of electric energy. However, after the economic development has reached a certain level, with the development of new energy sources and the improvement of energy utilization efficiency, the degree of dependence of economic development on electric energy will become smaller, together with the high-speed development of low-energy industries such as the tertiary industry. Therefore, there will be a phenomenon of negative correlation between Shandong economic development and electricity demand. Finally, the VAR model is used to test the regularity of the discrete second-order difference method. The VAR model constructed by the integrated data series can be seen from the stationarity of the time series data, the cointegration relationship between the two groups of integrated data and the index data of the VAR model. The data predicted by the discrete second-order difference method have high precision in regularity. Based on the numerical error rate analysis of the discrete second-order difference method, it can be seen that the data error rate predicted by the discrete second-order difference method is lower as a whole and shows a higher precision. Therefore, this paper uses the discrete second-order difference method to predict the Shandong power demand data has a high credibility, and provides data support and reference for the future development of Shandong electric power industry.
【学位授予单位】：天津大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：F426.61;F127

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