Xu Haiyan, Chang Yuqing, Zhao Yong, Wang Fuli
College of Information Science and Engineering, Northeastern University, Shenyang, 110819, China.
College of Resources and Civil Engineering, Northeastern University, Shenyang, 110819, China.
Environ Sci Pollut Res Int. 2022 Dec;29(58):87097-87113. doi: 10.1007/s11356-022-21904-5. Epub 2022 Jul 8.
Wind energy has become one of the most efficient renewable energy sources. However, the wind has the characteristics of intermittence and uncontrollability, so it is challenging to predict wind speed accurately. Considering the shortcomings of traditional wind power point predictions, a new hybrid model comprised three main modules used for data preprocessing, deterministic point prediction, and interval prediction is proposed to predict the wind speed interval. The first module, the data preprocessing module, uses variational mode decomposition (VMD), sample entropy (SE), and singular spectrum analysis (SSA) to extract the different frequency components of the initial wind speed series. The second module, the deterministic point prediction module, uses extreme learning machines (ELM), and a gated recursive unit (GRU) model to perform point prediction on the wind speed series. The third module, the interval prediction module, uses the nonparametric kernel density estimation method to construct the upper and lower bounds of the wind speed interval. In addition, the final wind speed prediction interval is obtained by integrating the prediction results of multiple interval prediction results to improve the robustness and generalization of the wind speed interval prediction. Finally, the effectiveness of the prediction performance of the proposed hybrid model is verified based on the data of two actual wind farms. The experimental results show that the proposed hybrid model can obtain the appropriate wind speed interval with high confidence and quality with different confidence levels of 95%, 90%, and 85%.
风能已成为最高效的可再生能源之一。然而，风具有间歇性和不可控性的特点，因此准确预测风速具有挑战性。考虑到传统风电功率点预测的缺点，提出了一种由数据预处理、确定性点预测和区间预测三个主要模块组成的新型混合模型来预测风速区间。第一个模块是数据预处理模块，使用变分模态分解（VMD）、样本熵（SE）和奇异谱分析（SSA）来提取初始风速序列的不同频率成分。第二个模块是确定性点预测模块，使用极限学习机（ELM）和门控递归单元（GRU）模型对风速序列进行点预测。第三个模块是区间预测模块，使用非参数核密度估计方法构建风速区间的上下界。此外，通过整合多个区间预测结果的预测结果来获得最终的风速预测区间，以提高风速区间预测的鲁棒性和泛化能力。最后，基于两个实际风电场的数据验证了所提出混合模型预测性能的有效性。实验结果表明，所提出的混合模型能够在95%、90%和85%的不同置信水平下，以高置信度和质量获得合适的风速区间。
