1 research outputs found
Application of a new information priority accumulated grey model with time power to predict short-term wind turbine capacity
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