基于神经网络的环境约束下我国煤炭需求预测

Prediction of coal demand in China under environmental constraints based on the neural network

我国经济快速发展带动煤炭消费快速增长的同时,也导致了严重的环境污染。因此,明确环境约束下,我国未来的煤炭需求对调整能源结构,协调经济增长与环境污染之间的平衡有重要意义。本文对多种煤炭需求预测方法进行比较论证,最终选取基于Matlab软件的BP神经网络方法进行煤炭长期需求量预测,然后基于《国家环境保护标准"十三五"发展规划》中一级空气质量标准和中国大陆地区环境大气在参照浓度为100μg/m^(3)时对污染物的年参照清除率计算出我国SO_(2)和烟(粉)尘的年排放量限额分别为3.23Mt和6.46Mt,再将环境压力参数代入模型,辅以经济角度和能源角度参数进行模型训练,预测出在预设环境压力下,高、中、低三种经济增长情境下2020-2050年我国煤炭资源需求量。结果表明,我国煤炭需求量在2025年左右达到峰值,约为3Btce,在2050年约为2Btce,与发达国家后期发展阶段的能源需求结构特征相符合。

The rapid development of China s economy drives the rapid growth of coal consumption,but also lead to serious environmental pollution.Therefore,it is of great significance to clarify the future coal demand under environmental constraints for China to adjust the energy structure and coordinate the balance between economic growth and environmental pollution.This paper compares and demonstrates a variety of coal demand forecasting methods,and finally chooses BP neural network method based on Matlab to forecast the long-term coal demand.Based on the first level air quality standard in the Outline of The 13th Five Year Plan for National Environmental Protection and the annual reference removal rate of pollutants in the reference concentration of 100μg/m 3 in the environmental atmosphere of China's Mainland,the annual emission limits of SO_(2) and smoke(dust)in China are calculated to be 3.23 Mt and 6.46 Mt respectively.The environmental pressure parameters are substituted into the model,supplemented by economic and energy angle parameters for model training,and then the coal demand in 2020-2050 under the preset environmental pressure,high,medium and low economic growth scenarios is predicted.The results show that China s coal demand peaked around 2025,about 3 Btce of standard coal,and China s coal demand is about 2 Btce in 2050,which is in line with the characteristics of energy demand structure in the later development stage of developed countries.