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Wind energy makes a significant contribution to global power generation. Predicting wind turbine capacity is becoming increasingly crucial for cleaner production. For this purpose, a new information priority accumulated grey model with time power is proposed to predict short-term wind turbine capacity. Firstly, the computational formulas for the time response sequence and the prediction values are deduced by grey modeling technique and the definite integral trapezoidal approximation formula. Secondly, an intelligent algorithm based on particle swarm optimization is applied to determine the optimal nonlinear parameters of the novel model. Thirdly, three real numerical examples are given to examine the accuracy of the new model by comparing with six existing prediction models. Finally, based on the wind turbine capacity from 2007 to 2017, the proposed model is established to predict the total wind turbine capacity in Europe, North America, Asia, and the world. The numerical results reveal that the novel model is superior to other forecasting models. It has a great advantage for small samples with new characteristic behaviors.Besides, reasonable suggestions are put forward from the standpoint of the practitioners and governments, which has high potential to advance the sustainable improvement of clean energy production in the future. . Application of a new information priority accumulated grey model with time power to predict short-term wind turbine capacity.

Improving the proportion of natural gas consumption of the manufacturing industry would make significant contributions to the low-carbon and sustainable development of China, which is one of the largest manufacturers in the world. However, it is very difficult to catch the trend of natural gas consumption of the concerning manufacturing industry as not enough trustable data can be collected. To fill this gap, a novel time-delayed fractional grey model is developed to forecast the natural gas consumption concerning time-delayed effect. Theoretical analysis shows it has more general formulation, unbiasedness and higher flexibility than the existing similar model. Being optimized by the Particle Swarm Optimization algorithm, the proposed model presents higher accuracy in four validation cases. Finally, it is used to forecast the natural gas consumption of the manufacturing industry of China, and the results show that the proposed model significantly outperforms the other seven existing grey models.