Forecasting Energy-Related CO2 Emissions Employing a Novel SSA-LSSVM Model: Considering Structural Factors in China

Abstract

Carbon dioxide (CO 2) emissions forecasting is becoming more important due to increasing climatic problems, which contributes to developing scientific climate policies and making reasonable energy plans. Considering that the influential factors of CO 2 emissions are multiplex and the relationships between factors and CO 2 emissions are complex and non-linear, a novel CO 2 forecasting model called SSA-LSSVM, which utilizes the Salp Swarm Algorithm (SSA) to optimize the two parameters of the least squares support sector machine (LSSVM) model, is proposed in this paper. The influential factors of CO 2 emissions, including the gross domestic product (GDP), population, energy consumption, economic structure, energy structure, urbanization rate, and energy intensity, are regarded as the input variables of the SSA-LSSVM model. The proposed model is verified to show a better forecasting performance compared with the selected models, including the single LSSVM model, the LSSVM model optimized by the particle swarm optimization algorithm (PSO-LSSVM), and the back propagation (BP) neural network model, on CO 2 emissions in China from 2014 to 2016. The comparative analysis indicates the SSA-LSSVM model is greatly superior and has the potential to improve the accuracy and reliability of CO 2 emissions forecasting. CO 2 emissions in China from 2017 to 2020 are forecast combined with the 13th Five-Year Plan for social, economic and energy development. The comparison of CO 2 emissions of China in 2020 shows that structural factors significantly affect CO 2 emission forecasting results. The average annual growth of CO 2 emissions slows down significantly due to a series of policies and actions taken by the Chinese government, which means China can keep the promise that greenhouse gas emissions will start to drop after 2030.

Forecasting the Short-Term Electric Load Considering the Influence of Air Pollution Prevention and Control Policy via a Hybrid Model

“…After standardizing the data, the parameters of the LSSVM optimized by SSA are calculated by using the MATALB software, and the regularization parameter C = 123.7584, the kernel function parameter σ 2 = 86.253. At this time, the fitness function value of the SSA-LSSVM model is 0.0282, that is, the model fitting accuracy can reach over 97% [42]. The process of optimizing the LSSVM parameters using SSA is shown in Figure 2.…”

“…Because the data in the sample set is characterized by high dimensionality, numerical type, and no markup, this paper employs the GRA method to classify the training sample set according to relevant references [37,40,42]. However, the traditional GRA method assumes that each feature variable in the sequence is equivalent, that is, the similarity degree of each feature variable has the same effect on the overall gray relation degree of the sequence.…”

“…In the SSA method, the initial population is divided into leaders and followers, and the leader leads the chain of the salp, followed by the followers. Assuming that there is a food source in the search space which is the salp chain target, called F, according to the behavior of the salp chain, the specific optimization steps are as follows [42,56,57]:…”

“…In order to solve the above problems, this paper analyzes the impact mechanism of APPC measures on short-term load and constructs a short-term load influence factor set accordingly. On this basis, a hybrid forecasting model, based on BWM [41] improved GRA and SSA-LSSVM [42] techniques, is proposed, and the effectiveness of the proposed model is verified by comparing with several selected load forecasting models. On the whole, the contributions of this paper mainly include:…”

Decomposition and Forecasting of CO2 Emissions in China’s Power Sector Based on STIRPAT Model with Selected PLS Model and a Novel Hybrid PLS-Grey-Markov Model

“…Ding et al [19] predicted the CO 2 emissions from fuel combustion by means of the original multi-variable grey approach. Zhao et al [20] applied the SSA-LSSVM (Least squares support vector machine for singular spectrum analysis) model to predict energy-related CO 2 emissions, indicating structural factors prominently affect CO 2 emission forecast results. Dai et al [21] used the GM (1,1) and LSSVM model to predict CO 2 emissions in China.…”