# Carbon Quota Allocation Prediction for Power Grids Using PSO-Optimized Neural Networks
## 2 Literature Review
While machine learning algorithms perform well in short-term data prediction, they often require extensive high-dimensional data. Many scholars have confirmed that combined prediction models outperform single models by integrating grey theory and neural networks. Meanwhile, during the learning process of neural networks, existing scholars usually combine other algorithms with them to improve the prediction accuracy. Some scholars have combined neural networks with particle swarm optimization algorithms to build PSO-BP prediction models [30; 31]. [PERSON] [32] developed the NNGM(1,1) model, a combination of grey theory and neural networks, and applied it to forecast electricity consumption using data from the Ministry of Energy and Natural Resources of Turkey and the Asia-Pacific Economic Cooperation. [PERSON] et al. [33] proposed an adaptive electricity load forecasting method by fusing neural networks with grey modeling, leveraging the complementary strengths of both techniques. [PERSON] et al. [34] employed a combination of particle swarm optimization, genetic algorithms, and BPNN models to forecast shallow groundwater levels in Harbin. [PERSON] and [PERSON] [35] devised a sparrow search algorithm-particle swarm optimization-back-propagation neural network (SSA-PSO-BPNN) model to precisely anticipate the deformation of aerospace wall plates during clamping. [PERSON] et al. [36] used the STIRPAT-GA-BPNN model optimized by a genetic algorithm to analyze the scenario prediction of the maximum CO2 emission in Dalian, China, and the accuracy of the predicted carbon emissions was as high as 99.33%, which proved its excellent prediction ability. [PERSON] et al. [37] proposed a technique that uses PSO in combination with the fusing of a one-dimensional convolutional neural network (CNN) and a multi-head self-attention (MHSA) bidirectional long short-term memory (BiLSTM) network called PSO-CNN-BiLSTM-MHSA. [PERSON] et al. [38] used a combination of particle swarm optimization algorithms as well as bat algorithms to obtain the hybrid particle swarm optimization (PSO) and a Bat algorithm (BA) (HPSOBA) and accurately predicted wind speeds and wind energy using cascaded feed-forward neural networks. The Carbon Emissions Trading Scheme (ETS) has significantly improved the pollution and CO\({}_{2}\) reductions of China's power sector, especially in terms of effective synergies in reducing CO\({}_{2}\), SO\({}_{2}\), and PM\({}_{2.5}\) emissions [39]. [PERSON] et al. [40] demonstrated that carbon trading improves carbon emission efficiency using an uncertainty-related stochastic DEED model. [PERSON] et al. [41] formulated a PEQ-based carbon quota allocation model for coal power supply chain enterprises. [PERSON] et al. [42] employed a combined trend prediction model and a triple-index allocated model for analyzing the provincial CO\({}_{2}\) emission allowance distribution of China's power system. Meanwhile, [PERSON] et al. [43] employed the Zero-Sum-Gain Data Envelopment Analysis model to construct a multi-criteria carbon quota allocation system for the power sector and utilized the CO\({}_{2}\) emission flow (CEF) model to track the \"virtual\" carbon flows in conjunction with electricity flows. [PERSON] et al. [44] designed a planning model to allocate carbon reduction shares among China's five largest power companies and evaluated their efficiency using the zero-sum gain data envelopment analysis (ZSG-DEA) method. [PERSON] et al. [45] established a Computable General Equilibrium (CGE) model to study China's electric power carbon allowance allocations and found that using historical emissions as a benchmark for quota allocation leads to better outcomes. [PERSON] et al. [46] developed a cross-regional electricity trading cooperation game model, finding that shared responsibility between producers and consumers for carbon quotas is more effective than placing the responsibility solely on producers, especially when low-carbon policies on the demand side are relaxed.