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

TL;DR

This paper introduces a new grey model with time power and an optimization algorithm to accurately predict short-term wind turbine capacity, outperforming existing models especially with small data samples.

Contribution

The paper proposes a novel grey prediction model combined with particle swarm optimization for enhanced accuracy in wind capacity forecasting.

Findings

The new model outperforms six existing models in accuracy.

It is particularly effective with small sample sizes.

The model successfully predicts wind capacity across different regions.

Abstract

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.