A novel local grey model considering fluctuation based on kernel function for energy production prediction

Abstract

The structure of energy production is constantly evolving, particularly in a low-carbon context, to align with the development of various industries. To pursue the global goal of carbon neutrality, it is essential to strike a balance between traditional energy and clean energy. Thus, it is of great practical significance to forecast different energy production effectively. Firstly, this paper proposes a novel local grey forecasting model to predict different energy production. Energy production has fluctuated in the past few years due to government policies. Secondly, the novel local grey forecasting model focuses on each year’s production in the modeling process and uses a kernel function to measure the validity of the information. Meanwhile, different from other grey models, the dynamic model proposed makes it possible that there are no static parameters for the overall prediction. Then, the window width in the local model is determined using the particle swarm optimization algorithm. Furthermore, energy production prediction including traditional energy and clean energy production in total North America, Europe and Asia Pacific is conducted. Results show that the novel local grey model has satisfactory prediction accuracy compared with other models, whose MAPEs have controlled under 5% for different energy production. Finally, reasonable suggestions for energy production are proposed to meet various energy needs.