# Carbon Quota Allocation Prediction for Power Grids Using PSO-Optimized Neural Networks
## 2 Literature Review  
As a key industry in the study of global CO\({}_{2}\) emissions, analyzing the current status of power sector emissions is essential for developing effective emission reduction strategies. Power-related carbon emissions issues have been the focus of much research in recent years by numerous academics, who have concentrated on both the types of emission generation and the factors that influence them. The emissions from the power sector can be categorized into direct emissions from electricity generation and indirect emissions from electricity demand [13]. [PERSON] et al. [14] proposed a method for calculating carbon emissions based on electricity consumption, while [PERSON] et al. [15] used a network approach to estimate carbon emissions from electricity consumption in 137 countries and regions around the world. [PERSON] et al. [16] and [PERSON] et al. [17] utilized various methods, including the carbon inventory approach and multi-region input-output approach, to calculate electricity-related carbon emissions. Greenhouse gas emissions from China's cross-provincial electricity trading were examined by [PERSON] et al. [18]. The influence of shifting electricity sources on carbon accounting was investigated by [PERSON] and [PERSON] [19]. [PERSON] et al. [20] studied the effect of energy trading on CO\({}_{2}\) emissions in the top 20 electricity trading nations in Europe. [PERSON] et al. [21]looked at the effects of power trading on air quality in the Yangtze River Basin. Similarly, [PERSON] [22] compared the impact of electricity trade on CO2 emission changes with other key factors, including energy intensity and economic performance. [PERSON] et al. [23] conducted a temporal and spatial decomposition study on the factors driving CO2 emission changes in China's thermal power industry from 2010 to 2020, revealing regional variability and trends in emission dynamics by expanding the Kaya constant equation and applying the LMDI decomposition method. Establishing an effective carbon emission prediction model (CEPM) to understand and forecast carbon dioxide emission trends is essential for mitigating climate change. Forecasting domestic and international carbon emissions from electricity relies primarily on quantitative methods, encompassing both individual and integrated forecasting models, including artificial neural networks, genetic algorithms, support vector machines, linear regression, and the GM(1,1) model [24]. [PERSON] et al. [25] presented continuous emission monitoring systems and predictive emission monitoring systems commonly utilized in power plants and highlighted that machine learning techniques are capable of significantly improving predictive emission monitoring systems via their ability to intelligently deal with large data sets, thereby transforming traditional emission monitoring systems by increasing the accuracy, effectiveness, and cost-efficiency. [PERSON] et al. [26] reviewed 147 CEPMs and noted the increasing use of neural network models, particularly feed-forward architectures, with many models incorporating meta-heuristic approaches. Parameter optimization was the main focus of prediction algorithms, followed by structural optimization. Predictor selection models employed grey relational analysis and principal component analysis for statistical and machine learning applications, effectively filtering influencing factors. [PERSON] et al. [27] constructed a new optimization model OGMW(1,1) based on the GM(1,1) model to predict a trend of China's primary sources of energy, oil and coal consumption, and CO2 emission in the next five years. [PERSON] et al. [28] proposed a random forest regression model enhanced by a genetic learning strategy to improve the traditional artificial bee colony algorithm for predicting CO2 emissions in the power system. [PERSON] and [PERSON] [29] addressed the issues of outliers and heteroskedasticity by applying a grey theory prediction model with nonlinear inflection point forecasting.