School of Control and Computer Engineering, North China Electric Power University, Baoding, 071003, Hebei, China.
Environ Sci Pollut Res Int. 2022 Aug;29(38):58097-58109. doi: 10.1007/s11356-022-19885-6. Epub 2022 Apr 1.
Aiming at the uncertainty of wind power and the low accuracy of multi-step interval prediction, an ultra-short-term wind power multi-step interval prediction method based on complete ensemble empirical mode decomposition with adaptive noise-fuzzy information granulation (CEEMDAN-FIG) and convolutional neural network-bidirectional long short-term memory (CNN-BiLSTM) is proposed. Firstly, the CEEMDAN is used to decompose the wind power time series into several sub-components to reduce the non-stationary characteristics of the wind power time series. Then, different components are selected for FIG, and the maximum value sequence, average value sequence, minimum value sequence gotten from FIG, and the remaining components without FIG are combined with the wind speed data, wind direction data, and the temperature data. They all are input into the CNN-BiLSTM combined prediction model to obtain the initial wind power prediction interval. The prediction results of the maximum value sequence, the average value sequence, and the minimum value sequence are respectively superimposed on the prediction results of the remaining components to obtain the upper limit, point prediction, and lower limit of the initial prediction interval. Finally, the improved coverage width criterion is used as the objective function to optimize the interval, and the forecast interval of wind power under a given confidence level is generated. Taking the actual operating data of a certain unit of a wind farm as an example, the validity of the proposed model is verified.
针对风力发电的不确定性和多步区间预测精度低的问题，提出了一种基于完全集合经验模态分解自适应噪声模糊信息粒度（CEEMDAN-FIG）和卷积神经网络双向长短时记忆（CNN-BiLSTM）的超短期风力多步区间预测方法。首先，利用 CEEMDAN 将风力发电时间序列分解为几个子分量，以降低风力发电时间序列的非平稳特性。然后，对不同的分量进行 FIG 处理，得到 FIG 得到的最大值序列、平均值序列、最小值序列，以及未经过 FIG 处理的剩余分量，将这些分量与风速数据、风向数据和温度数据结合，全部输入到 CNN-BiLSTM 联合预测模型中，得到初始风力发电预测区间。将最大值序列、平均值序列和最小值序列的预测结果分别叠加在剩余分量的预测结果上，得到初始预测区间的上限、点预测和下限。最后，采用改进的覆盖宽度准则作为目标函数对区间进行优化，生成在给定置信水平下的风力发电预测区间。以某风电场某机组的实际运行数据为例，验证了所提模型的有效性。
