With power consumption increasing in China, the CO2 emissions from electricity pose a serious threat to the environment. Therefore, it is of great significance to explore the influencing factors of power CO2 emissions, which is conducive to sustainable economic development. Taking the characteristics of power generation, transmission and consumption into consideration, the grey relational analysis method (GRA) is adopted to select 11 influencing factors, which are further converted into 5 main factors by hierarchical clustering analysis (HCA). According to the possible variation tendency of each factor, 48 development scenarios are set up from 2018–2025, and then an extreme learning machine optimized by whale algorithm based on chaotic sine cosine operator (CSCWOA-ELM) is established to predict the power CO2 emissions respectively. The results show that gross domestic product (GDP) has the greatest impact on the CO2 emissions from power output, of which the average contribution rate is 1.28%. Similarly, power structure and living consumption level also have an enormous influence, with average contribution rates over 0.6%. Eventually, the analysis made in this study can provide valuable policy implications for power CO2 emissions reduction, which can be regarded as a reference for China’s 14th Five-Year development plan in the future.
Grey Relational Grade Based Quantitative Analysis of the Factors Influencing the Load Characteristics of A Power Grid
