基于灰色预测模型的参数寻优方法及能源预测应用.

Energy and electricity are the key area for China to achieve its dual-carbon goals. Precisely predicting future energy supply and demand and carbon emissions are beneficial for formulate a feasible path for low-carbon transition. The gray prediction model GM(1,1) is the most widely used dynamic prediction model in the field of energy forecasting, but it has higher requirements for original data and the model may fail when the development coefficient of GM(1,1) is large. On the other hand, another key parameter of GM(1,1), the gray effect u, directly determines the prediction accuracy of the model. If a better value of u can be found to be substituted into the model for prediction, the accuracy of the model will be significantly improved. Taking these into account problem, this paper introduced into the optimization process of the gray prediction model a novel swarm intelligence algorithm, namely the Monarch Butterfly Optimization (MBO) that performs well in actual optimization problems. The newly proposed gray-monarch butterfly prediction model can achieve accurate predictions of Tianjin's energy supply and demand and carbon emissions. Based on the prediction results, a low-carbon transition path for Tianjin's carbon peak in 2030 was formulated. Compared with existing classic literature methods and prediction data, the effectiveness and superiority of the grey-monarch butterfly optimized forecasting model proposed in this paper were discussed. [ABSTRACT FROM AUTHOR]