Forecasting Global Solar Energy Consumption Using Conformable Fractional Incomplete Gamma Grey Model

Solar energy has become the core driver of global energy transformation. To achieve a more accurate prediction of the global solar energy consumption, this study presents a novel conformable fractional incomplete gamma grey model (denoted as CFIGGM). In this new model, the conformable fractional-order accumulation operator is introduced to fully mine the information of small samples and reduce the dependence on data distribution. Meanwhile, the Whale Optimization Algorithm is also utilized to solve the optimal value of nonlinear parameters in the newly proposed model to enhance its prediction performance. Moreover, numerical experiments are carried out on five sequences to verify the performance of the new model. The experiments’ results show that the proposed model has better prediction performance than the comparative models. Then, the new model is applied to forecast the global solar energy consumption. The fitting MAPE of the newly proposed model is 0.07% on the training set, and the prediction MAPE is 0.78% on the test set. As an application, the trend in global solar energy consumption is predicted by using the proposed model. Its result shows that the global solar energy consumption is projected to maintain a strong growth momentum, but the growth rate will slow down in the future. The results can serve as strong supporting data for relevant departments and enterprises.