RETRACTED: Wind energy resource assessment based on joint wolf pack intelligent optimization algorithm

TL;DR

This paper proposes a new method for assessing wind energy resources using an improved wolf pack algorithm and hybrid prediction models to improve accuracy and efficiency.

Contribution

The novel contribution is the integration of a pollination-enhanced wolf pack algorithm with data decomposition and ARIMA for wind speed prediction.

Findings

The Weibull model achieved high accuracy in wind speed data fitting with an average coefficient of determination of 0.96.

The hybrid prediction model outperformed existing models with a maximum deviation of less than 3% and a coefficient of determination near 1.0.

The proposed method has low time complexity and completes computations in under 5 seconds across different data scales.

Abstract

Wind energy is a clean and renewable energy source with great potential for development, but the intermittent and stochastic characteristics of wind speed have brought great challenges to the effective development and utilisation of wind energy resources, resulting in high development costs. Therefore, how to accurately assess the wind energy resources and effectively predict the wind speed has become a key issue to be solved in the current wind energy field. In view of this, the study proposes the Weibull model to model the wind speed data, and then introduces the wolf pack intelligent optimisation algorithm and improves it through the pollination mechanism to improve the accuracy of wind energy resource assessment. Secondly, considering the complexity and diversity of wind speed data characteristics, data decomposition technique, autoregressive moving average (ARIMA) model and cuckoo search algorithm are used to achieve data preprocessing, serial data modelling and hybrid prediction. The experimental results show that the Weibull model has good fitting accuracy for wind speed data, with residual sum of squares, RMSE, and average coefficient of determination of 0.05, 0.014, and 0.96, respectively, accurately reflecting the statistical characteristics of wind speed data. The wind speed prediction performance of the hybrid prediction model is good, with a maximum deviation of no more than 3% from the true value, which is significantly better than the compared VMD-ISOA-KELM model and CNN-BLSTM model, and its prediction error is relatively small. The hybrid prediction model has a smaller relative error value compared to a single algorithm, with a maximum value of less than 0.2. It has better prediction performance than the combination model, with a coefficient of determination approaching 1.0, a fitting accuracy of 0.994, a mean square error of 0.1947, a root mean square error of 0.3847, and an average absolute percentage error of 15.23%. And the research method can effectively evaluate the status of wind energy resources, with low time complexity at different data scales, taking no more than 5 seconds, and improving operational efficiency. This research method can provide strong technical support and reference basis for the development and utilisation of wind energy resources, and help to promote the sustainable development of wind energy industry.