Research on price forecasting method of China's carbon trading market based on PSO-RBF algorithm

The forecasting of carbon emissions trading market price is the basis for improving risk management in the carbon trading market and strengthening the enthusiasm of market participants. This paper will apply machine learning methods to forecast the price of China's carbon trading market. First, the daily average transaction prices of the carbon trading market in Hubei and Shenzhen are collected, and these data are preprocessed by PCAF approach to choose the input variables. Second, a prediction model based on Radical Basis Function (RBF) neural network is established and the parameters of the neural network are optimized by Particle Swarm Optimization (PSO). Finally, the PSO-RBF model is validated by the actual data and proved that the PSO-RBF model has better prediction effect than BP and RBF neural network in China's carbon prices prediction. It is indicated that the prediction model has more significant applicability and deserves further popularization.