基于二层分解的PSO-LSTM模型风电功率超短期预测

为提升风电功率预测精度，提出基于二层分解技术和粒子群优化长短期记忆（PSO-LSTM）神经网络组合的超短期风电功率预测模型。对风电功率原始数据，采用快速集合经验模态分解（FEEMD）方法将其分解为一系列本征模态函数（IMF）分量和余项，针对高频分量采用变分模态分解（VMD）进行二层分解。运用样本熵来解决分量个数过多、计算量繁杂的问题。通过偏自相关函数(PACF)筛选出与预测值关联程度高的元素确定输入维数。最后，选用PSO来优化LSTM相关参数建立预测模型并叠加获得最终值。试验结果表明，该组合模型有效提高了预测精度。

Ultra-Short-Term Wind Power Prediction Based on Two-Layer Decomposition Technique and PSO-LSTM Model

In order to improve wind power prediction accuracy, an ultra-short-term wind power prediction model based on the combination of two-layer decomposition technique and particle swarm optimization long short-term memory (PSO-LSTM) neural network is proposed. The fast ensemble empirical mode decomposition (FEEMD) method is used to deconstruct the original wind power sequence into a series of intrinsic mode function (IMF) components and the remainder term. The high frequency IMF is decomposed by the variational mode decomposition (VMD) of two-layer decomposition technology. The sample entropy is used to solve the problem of too many components and complicated calculation. The input dimension is determined by selecting the elements of high correlation degree with the predicted value through partial autocorrelation coefficient function (PACF). PSO LSTM is used to construct the prediction model, and the final value is obtained by superposition. The experimental results show that the combined model can effectively improve the prediction accuracy.