中国火电行业多模型碳达峰情景预测

doi:10.12204/j.issn.1000-7229.2022.05.003

摘要/Abstract

摘要：

随着2030年碳达峰目标的提出,能源行业中火电行业的碳达峰情况备受瞩目。文章首先根据Kaya恒等式的扩展,分析得到影响碳排放的主要因素:人口、经济、产业结构、能源消费强度以及消费结构;其次,以2000—2018年数据为基础分别建立线性回归、径向基函数(radial basis function, RBF)神经网络、差分自回归移动平均(autoregressive integrated moving average, ARIMA)以及BP神经网络模型,对比得到最优的预测模型;最后,基于最优模型在基准发展、产业优化、技术突破、低碳发展这4种不同发展情景下对2021—2050年碳排放量进行预测,然后在此基础上对碳达峰情况进行分析。结果表明:低碳发展情景的碳达峰时间最早且峰值最低,是中国火电行业实现碳排放达峰的首选发展模式,为推动火电行业尽快实现较低的碳排放峰值提供借鉴。

关键词: 碳达峰预测, 情景分析, 线性回归, 差分自回归移动平均(ARIMA), 神经网络

Abstract:

With the goal of carbon peaking in 2030, the carbon peak of thermal power industry in the energy industry has attracted much attention. In this paper, firstly, according to the extension of Kaya’s constant equation, the main factors affecting carbon emissions are analyzed and obtained: population, economy, industrial structure, energy consumption intensity and energy consumption structure. Secondly, linear regression, RBF neural network, ARIMA and BP neural network models are established according to data from 2000 to 2018, to get the optimal prediction models by comparison. Finally, on the basis of the optimal model, carbon emissions from 2021 to 2050 are predicted under four different development scenarios: baseline development, industrial optimization, technological breakthrough, and low-carbon development, and then carbon peak situation is analyzed on this basis. The results show that it has the earliest peak time and lowest peak value in the low-carbon development scenario, which is the preferred mode to achieve peak carbon emissions in China’s thermal power industry, and provides a reference for promoting the thermal power industry to achieve lower peak carbon emissions as soon as possible.

Key words: carbon peak forecast, scenario analysis, linear regression model, autoregressive integrated moving average (ARIMA), neural network

中图分类号:

TM61
X22

张金良, 贾凡. 中国火电行业多模型碳达峰情景预测[J]. 电力建设, 2022, 43(5): 18-28.

ZHANG Jinliang, JIA Fan. Multi-model Carbon Peak Scenario Prediction for Thermal Power Industry in China[J]. ELECTRIC POWER CONSTRUCTION, 2022, 43(5): 18-28.

图/表 9

表1 变量符号表

Table 1 Variable symbol table

图1 RBF神经网络结构

Fig.1 RBF neural network structure

图2 BP神经网络结构

Fig.2 BP neural network structure

图3 技术路线图

Fig.3 Technology roadmap

图4 模型对比图

Fig.4 Model comparison chart

表2 预测误差对比表

Table 2 Comparison table of forecast error

表3 情景设置

Table 3 Scenario Setting

表4 情景设置参数

Table 4 Scenario setting parameters %

图5 情景预测对比图

Fig.5 Comparison chart of scenario prediction

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