Carbon Price Prediction based on Genetic Algorithm Optimized BP Neural Network

With the launch of the carbon emissions trading system, the prediction of carbon prices provides a basis for those in power to formulate relevant policies and provides potential risk mechanisms for carbon market participants. Aiming at the disadvantages of the traditional BP neural network that the convergence speed is slow and it is easy to fall into the local extreme value, this paper constructs a carbon price prediction model based on the genetic algorithm to optimize the BP neural network, which can predict the carbon price quickly and accurately. Through analysis of primary and secondary factors, coal prices, oil prices, natural gas prices, and electricity prices are selected as the input to the network. The initial weights and thresholds obtained by the genetic algorithm search can accurately predict the carbon price. Through the relevant verification of the test data, the accuracy and stability of the BP neural network carbon price prediction model optimized by the genetic algorithm have reached expectations.