College of Water Conservancy and Hydropower, Handan 056038, China.
Low carbon Handan Clean Energy Technology Co., Ltd, Handan 0056000, China.
An Acad Bras Cienc. 2024 Oct 21;96(4):e20230891. doi: 10.1590/0001-3765202420230891. eCollection 2024.
Accurate wind power prediction can effectively alleviate the pressure of the power system peak frequency regulation, and is more conducive to the economic dispatch of the power system. To enhance wind power forecasting accuracy, a hybrid approach for wind power interval prediction is proposes in this study. Firstly, an Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) is applied to decompose the initial wind power sequence into multiple modes, and Variational Mode Decomposition is used to further decompose the high-frequency non-stationary components. Next, Fuzzy Entropy (FE) is utilized to assess the complexity of the post-decomposed Intrinsic Mode Functions (IMFs), and different forecasting methods are employed accordingly, the point predictions were obtained by linearly summing the component predictions.Additionally, an improved sparrow search algorithm (ISSA) is used to seek the optimal hyperparameters of the prediction algorithm. Finally, the prediction intervals are constructed using the point prediction results based on kernel density estimation (KDE). The root mean square errors (RMSE) of deterministic predictions are 2.8458 MW and 1.8605 MW, with uncertainty coverage rates of 95.83% and 97.92% at a 95% confidence level.
准确的风力预测可以有效缓解电力系统峰频调节的压力，更有利于电力系统的经济调度。为了提高风力预测精度，本研究提出了一种混合方法进行风力区间预测。首先，应用改进的完全集成经验模态分解自适应噪声（ICEEMDAN）将初始风力序列分解为多个模态，并进一步应用变分模态分解分解高频非平稳分量。接下来，利用模糊熵（FE）评估分解后的固有模态函数（IMFs）的复杂度，并根据复杂度采用不同的预测方法，通过线性叠加分量预测得到点预测结果。此外，采用改进的麻雀搜索算法（ISSA）寻找预测算法的最优超参数。最后，基于核密度估计（KDE）利用点预测结果构建预测区间。在 95%置信水平下，确定性预测的均方根误差（RMSE）分别为 2.8458MW 和 1.8605MW，不确定性覆盖率分别为 95.83%和 97.92%。
