贵州发电侧低碳效益关键因素建模与实证研究

Modeling and Empirical Research on Key Factors of Low Carbon Efficiency in Guizhou Power Generation Side

清洁能源发电在促进传统电力行业向低碳化电力行业转型方面起着十分重要的作用。因此,转变传统的发电结构,降低火电厂CO_2的排放量,提高清洁能源发电的比例,对实现低碳效益最大化起着十分重大的理论意义和现实意义。为此,本文首先阐述了发电侧低碳效益因素的选取原则和关键因素,其次运用回归分析、指数平滑法和NAR动态神经网络对贵州发电量进行了训练分析,再次利用NAR动态神经网络对贵州省发电量、火力发电量和火电供电煤耗进行了预测,最后结合"十三五"规划目标,研究了2018—2020年贵州发电侧的低碳效益,并提出了相应的政策性建议。

Power generation by clean energy plays an important role in promoting the transformation from traditional power industry to low-carbon power industry. Therefore, it is of great theoretical and practical significance to change the traditional power generation structure, reduce CO2 emissions of thermal power plants and increase the proportion of clean energy generation to achieve the maximum of low-carbon benefits. Firstly, the selection principle and key factors of low carbon benefit factors on power generation side are expounded;secondly, the power generation in Guizhou is trained and analyzed by regression analysis, exponential smoothing method and NAR dynamic neural network. And then, the NAR dynamic neural network is used to forecast the power generation, thermal power generation and coal consumption of thermal power supply in Guizhou Province. Finally, combined with the 13th Five-Year Plan goal, the low-carbon benefits of Guizhou power generation side in 2018-2020 are studied, and the corresponding policy recommendations are put forward.