融合 CEEMDAN 和 ICS-LSTM 的短期风速预测建模

为提高风速预测精度，本文从挖掘风速数据可预测性和优化预测模型性能两方面出发，提出一种融合完全经验模态分解(CEEMDAN)和改进的布谷鸟算法优化长短期记忆深度神经网络(ICS-LSTM)的风速预测模型。首先采用CEEMDAN降低风速序列的不稳定性，提高其可预测性。其次对分解得到的各子序列建立LSTM预测模型，并采用ICS优化LSTM的关键参数，提高LSTM预测模型的回归性能。然后对各个子序列采用最优参数LSTM预测模型进行建模预测，最后叠加子序列预测结果得到风速预测结果。经实测数据验证，本文所提模型的平均绝对误差和平均相对误差仅为0.82和0.95,对比研究表明本文所提预测模型的优越性。

Short-term wind speed forecasting modeling integrating CEEMDAN and ICS-LSTM

In order to improve the accuracy of wind speed prediction, this paper starts from the predictability of mining wind speed data and optimizes the performance of the prediction model, and proposes a prediction model that combines adaptive noise complete empirical mode decomposition ( CEEMDAN) and long short-term memory neural network ( LSTM). First, CEEMDAN is used to reduce the instability of the wind speed sequence and improve its predictability. Secondly, a LSTM prediction model is established for each subsequence obtained by decomposition, and an improved cuckoo search algorithm (ICS) is used to optimize the key parameters of LSTM and improve its regression performance. Then use the optimal parameter LSTM prediction model for each sub-sequence to model and predict, and superimpose to obtain the wind speed prediction result. Verified by actual measurement data, the average absolute error and average relative error of the model proposed in this paper are only 0. 82 and 0. 95. The comparative study shows that the proposed prediction model is scientific and advanced.