School of Management, Wuhan University of Science and Technoloy, Wuhan, China.
PLoS One. 2023 Sep 8;18(9):e0289161. doi: 10.1371/journal.pone.0289161. eCollection 2023.
Wind energy, as a kind of environmentally friendly renewable energy, has attracted a lot of attention in recent decades. However, the security and stability of the power system is potentially affected by large-scale wind power grid due to the randomness and intermittence of wind speed. Therefore, accurate wind speed prediction is conductive to power system operation. A hybrid wind speed prediction model based on Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN), Multiscale Fuzzy Entropy (MFE), Long short-term memory (LSTM) and INFORMER is proposed in this paper. Firstly, the wind speed data are decomposed into multiple intrinsic mode functions (IMFs) by ICEEMDAN. Then, the MFE values of each mode are calculated, and the modes with similar MFE values are aggregated to obtain new subsequences. Finally, each subsequence is predicted by informer and LSTM, each sequence selects the one with better performance than the two predictors, and the prediction results of each subsequence are superimposed to obtain the final prediction results. The proposed hybrid model is also compared with other seven related models based on four evaluation metrics under different prediction periods to verify its validity and applicability. The experimental results indicate that the proposed hybrid model based on ICEEMDAN, MFE, LSTM and INFORMER exhibits higher accuracy and greater applicability.
风能作为一种环保可再生能源，在近几十年中受到了广泛关注。然而，由于风速的随机性和间歇性，大规模风力发电网会对电力系统的安全性和稳定性产生潜在影响。因此，准确的风速预测有助于电力系统的运行。本文提出了一种基于改进完全集合经验模态分解自适应噪声（ICEEMDAN）、多尺度模糊熵（MFE）、长短期记忆（LSTM）和 INFORMER 的混合风速预测模型。首先，通过 ICEEMDAN 将风速数据分解为多个固有模态函数（IMF）。然后，计算每个模态的 MFE 值，并将具有相似 MFE 值的模态聚合以获得新的子序列。最后，通过 INFORMER 和 LSTM 分别对每个子序列进行预测，每个序列选择比两个预测器性能更好的一个，然后将每个子序列的预测结果叠加，得到最终的预测结果。基于四个不同预测时间段的评价指标，将所提出的混合模型与其他七个相关模型进行了比较，验证了其有效性和适用性。实验结果表明，基于 ICEEMDAN、MFE、LSTM 和 INFORMER 的所提出的混合模型具有更高的准确性和更大的适用性。
