Application of Radial Basis Function Neural Network for Carbon Price Forecasting

Abstract

This paper proposes a carbon price forecasting system for the participants to quickly and accurately predict the carbon price. 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 by year and season. The Radial Basis Function Neural Network (RBFNN) is constructed in the searching process. The optimal parameters obtained from the RBFNN enable the learning rate parameters to regulate and improve the predicting errors during the training process, enhancing the accuracy and reliability of predictions. By linking the RBFNN and Excel Database, the training stages of the RBFNN retrieve the input data from the Excel Database so that the efficiency and accuracy of the predicting system can be analyzed. Simulation results in this paper will provide an accurate and real-time method for participants to forecast carbon price and raise the market competition in a carbon trading market.