Wang Jiayuan
Gansu Water Resources and Hydropower Survey Design and Research Institute Co., Ltd., Lanzhou, China.
PLoS One. 2025 Jun 27;20(6):e0326035. doi: 10.1371/journal.pone.0326035. eCollection 2025.
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.
风能是一种具有巨大发展潜力的清洁可再生能源，但风速的间歇性和随机性特征给风能资源的有效开发利用带来了巨大挑战，导致开发成本高昂。因此，如何准确评估风能资源并有效预测风速已成为当前风能领域亟待解决的关键问题。鉴于此，该研究提出用威布尔模型对风速数据进行建模，然后引入狼群智能优化算法并通过授粉机制对其进行改进，以提高风能资源评估的准确性。其次，考虑到风速数据特征的复杂性和多样性，采用数据分解技术、自回归移动平均（ARIMA）模型和布谷鸟搜索算法实现数据预处理、序列数据建模和混合预测。实验结果表明，威布尔模型对风速数据具有良好的拟合精度，残差平方和、均方根误差和平均决定系数分别为0.05、0.014和0.96，准确反映了风速数据的统计特征。混合预测模型的风速预测性能良好，与真实值的最大偏差不超过3%，明显优于对比的VMD-ISOA-KELM模型和CNN-BLSTM模型，且其预测误差相对较小。与单一算法相比，混合预测模型的相对误差值更小，最大值小于0.2。它比组合模型具有更好的预测性能，决定系数接近1.0，拟合精度为0.994，均方误差为0.1947，均方根误差为0.3847，平均绝对百分比误差为15.23%。并且该研究方法能够有效评估风能资源状况，在不同数据规模下时间复杂度较低，耗时不超过5秒，提高了运行效率。该研究方法可为风能资源的开发利用提供有力的技术支持和参考依据，有助于推动风能产业的可持续发展。
