基于GA-VMD-ESN的光伏功率组合预测模型

Photovoltaic Power Combination Prediction Model Based on GA-VMD-ESN

为了降低光伏发电的随机性、波动性和间歇性对电网调度和经济效益的影响,提出了一种光伏功率预测的组合模型。基于遗传算法(GA)优化变模态分解(VMD)的关键参数,该模型首先将光伏功率的非平稳时间序列进行VMD分解,并通过GA优化模态数量和惩罚系数,以确保分解效果最佳。针对每个模态的波动特性,应用回声状态网络(ESN)进行建模。最终,将各模态的预测结果进行叠加重构,得到光伏功率的最终预测值。通过实验验证,证明了该组合预测模型在精度和性能上的优越性。

In order to reduce the impact of the randomness,volatility,and intermittency of photovoltaic power generation on grid scheduling and economic benefits,a combined model for photovoltaic power prediction is proposed.Based on genetic algorithm(GA)to optimize the key parameters of variable mode decomposition(VMD),this model first decomposes the non-stationary time series of photovoltaic power into VMD,and optimizes the number of modes and penalty coefficients through GA to ensure the best decomposition effect.Apply Echo State Network(ESN)for modeling the fluctuation characteristics of each modality.Finally,the predicted results of each mode are superimposed and reconstructed to obtain the final predicted value of photovoltaic power.Through experimental verification,it has been proven that the combined prediction model has superior accuracy and performance.