基于集合经验模态分解和套索算法的短期风速组合变权预测模型研究

准确的风速预测对风电场实现平稳出力具有重要意义。为提高短期风速预测精度,提出一种基于集合经验模态分解(Ensemble Empirical Mode Decomposition,EEMD)、套索算法(Least Absolute Shrinkage and Selection Operator, LASSO)、遗传算法(Genetic Algorithm, GA)、广义回归神经网络(General Regression Neural Network, GRNN)和长短期记忆模型(Long Short-Term Memory,LSTM)的短期风速变权组合预测模型(Variable Weighted Hybrid Model, VWHM)。首先运用集合经验模态分解技术,将原始风速时间序列分解成多个不同的子序列。然后运用套索算法对各个子序列的数据变量进行筛选,提取代表性变量作为预测输入。最后利用GA的全局优化能力,对由GRNN和LSTM构成的组合预测模型的权重系数进行移动样本自适应变权求解,并加权得到最终预测结果。仿真结果表明,所提的变权组合模型比单一模型以及传统组合模型具有更高的预测精度,且在风速预测中具有优越性。

Research on short-term wind speed hybrid variable weight prediction model based on ensemble empirical mode decomposition and LASSO algorithm

Accurate wind speed prediction is significant for wind farm development and utilization of wind energy. In order to improve the prediction accuracy of short-term wind speed, a combined prediction model with variable weight of short-term wind speed based on Ensemble Empirical Mode Decomposition (EEMD), Least Absolute Shrinkage and Selection Operator (LASSO), Genetic Algorithm (GA), General Regression Neural Network (GRNN) and long-term and short-term memory is proposed. First, the ensemble empirical mode decomposition technique is used to decompose the original wind speed time series into multiple sub-sequences. Then, using the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm, the historical data of each sub-sequence are filtered, and representative variables are extracted as prediction inputs. Finally, using the global optimization ability of a GA, the weight coefficients of the combined prediction model composed of GRNN and LSTM are adaptively solved by moving samples, and the final prediction results are obtained by weighting. The simulation results show that the proposed variable weight combination model has higher prediction accuracy than a single model and a traditional combination model, and has superiority in wind speed prediction. This work is supported by National Natural Science Foundation of China (No. 61973117).