基于LCD-SE-IWOA-KELM的短期风电功率区间预测

针对风电功率的不确定性、随机性以及已有的风电功率点预测无法反应其不确定性信息的问题,提出了基于局部特征尺度分解(LCD)-样本熵(SE)和改进鲸鱼优化算法(IWOA)优化核极限学习机(KELM)的短期风电功率区间预测模型。采用LCD分解来降低原始风电功率序列的非平稳性,通过测量各ISC分量的样本熵来重构新的序列以降低过多的分量对预测精度带来的影响,然后分别建立各新序列的区间预测模型,最后将各新序列的预测结果进行叠加获得最终预测结果。采用改进的WOA算法优化核极限学习机的参数。实验仿真表明,文中所提模型能够获得良好的区间预测结果,具有一定的实际意义和应用价值。

Short-term wind power interval prediction based on LCD-SE-IWOA-KELM

In view of the uncertainty and randomness of wind power and the existing wind power point prediction can not reflect its uncertainty information, a short-term wind power interval prediction model based on local characteristic-scale decomposition (LCD) -sample entropy (SE) and improved whale optimization algorithm (IWOA) -kernel extreme learning machine (KELM) was proposed. LCD decomposition is used to reduce the non-stationarity of the original wind power sequence. By measuring the sample entropy of each ISC component, the new sequence is reconstructed to reduce the influence of excessive components on the prediction accuracy, and then the interval of each new sequence is established. The prediction model is finally superimposed on the prediction results of each new sequence to obtain the final prediction result. The improved WOA algorithm is used to optimize the parameters of the kernel extreme learning machine. The experimental simulations show that the proposed model can obtain good interval prediction results, which has certain practical significance and application value.