改进黑猩猩算法和LSSVR-BiLSTM双尺度模型的短期风功率预测

Short-term wind power prediction based on improved chimp algorithm and LSSVR-BiLSTM dual scale model

为提高风功率预测精度,提出一种基于改进自适应白噪声完全集合经验模态分解(ICEEMDAN)、排列熵(PE)、改进黑猩猩优化算法(ICHOA)、最小二乘支持向量回归机(LSSVR)和双向长短时记忆(BiLSTM)网络相结合的短期风功率预测混合模型。通过ICEEMDAN将非平稳的原始风电序列分解为相对平稳的模态分量,并使用PE聚合来降低计算复杂度。分别将BiLSTM模型和LSSVR模型应用于高频分量和低频分量的预测。采用ICHOA用于优化模型的参数。将每个预测分量值叠加得出最终预测结果。算例分析结果表明,所提LSSVR-BiLSTM双尺度深度学习模型与其他模型相比,能更好地拟合风功率数据,具有较高的预测精度和可行性。

Accurate wind power prediction is crucial for the efficient and safe operation of the power system.To improve the accuracy of wind power prediction,a short-term mixture model for wind power prediction was proposed based on the combination of improved complete ensemble empirical modal decomposition with adaptive white noise(ICEEMDAN),permutation entropy(PE),improved chimp optimization algorithm(ICHOA),least squares support vector regression(LSSVR)and bi-directional long short memory(BiLSTM)network.Firstly,the non-stationary original wind power sequence is decomposed into relatively stationary modal components through ICEEMDAN,and PE aggregation is used to reduce computational complexity.Secondly,the BiLSTM model and LSSVR model are applied to predict high-frequency and low-frequency components,respectively.ICHOA is used to optimize the parameters of the model.Finally,the final prediction result is obtained by overlaying the values of each predicted component.Through the analysis of specific examples,the proposed LSSVR-BiLSTM dual scale deep learning model is compared with other models,which can better fit the wind power data and has higher prediction accuracy and feasibility.