A Forecasting System of Carbon Price in the Carbon Trading Markets Using Artificial Neural Network

In this paper, a carbon price forecasting system is proposed to quickly and accurately predict the carbon price for participants. The data including the carbon trading price, oil price, coal price and gas price are first calculated and the data clusters are embedded in the Excel Database. Based on the Radial Basis Function Network (RBFN) and Ant Colony Optimization (ACO), an Ant-Based Radial Basis Function Network (ARBFN) is constructed in the searching process. The optimal parameters obtained from the ACO enable the learning rate parameters to regulate and improve the predicting errors during the training process. By linking the ARBFN and Excel database, the training stages of the ARBFN retrieve the input data from the Excel Database so that the efficiency and accuracy of the predicting system can be analyzed. A comparison of the Back-propagation Neural Network (BPN), Radial Basis function (RBFN), Probability Neural Network (PNN) and the ARBFN show that the converging solution is obtained by the prediction process. Simulation results will provide an accurate and real-time method for participants to forecast carbon price and raise the market competition in a carbon trading market.