Multi-model Carbon Peak Scenario Prediction for Thermal Power Industry in China

doi:10.12204/j.issn.1000-7229.2022.05.003

Abstract

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

CLC Number:

TM61
X22

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.

Figures/Tables 9

Table 1 Variable symbol table

Fig.1 RBF neural network structure

Fig.2 BP neural network structure

Fig.3 Technology roadmap

Fig.4 Model comparison chart

Table 2 Comparison table of forecast error

Table 3 Scenario Setting

Table 4 Scenario setting parameters %

Fig.5 Comparison chart of scenario prediction

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