基于混沌特征改进鲸鱼优化算法-相关向量机的超短期光伏发电输出功率预测

A Method to Forecast Ultra-Short-Term Output of Photovoltaic PowerGeneration Based on Chaotic Characteristic-Improved WhaleOptimization Algorithm and Relevance Vector Machine

通过挖掘光伏序列的混沌特性,提出一种基于混沌特征改进的鲸鱼优化算法(whale optimization algorithm,WOA)相关向量机(relevance vector machine,RVM)预测方法,确定RVM参数与光伏发电输出功率混沌特征间的物理联系。首先,利用伪近邻法和复自相关法计算光伏序列的混沌参数,重构相空间,确定RVM高斯核的径向范围;然后,利用WOA对RVM参数进行寻优,提高核函数泛化能力和收敛速度,完善超短期预测方法;最后,运用实际光伏发电输出功率数据,仿真分析5个典型日的预测效果。结果表明,所提方法在不同天气情况下具有良好的预测准确度和适应性。

By means of mining the chaotic characteristic of photovoltaic(PV)sequence,a forecasting method based on chaotic characteristic-improved whale optimization algorithm(abbr.WOA)and relevance vector machine(abbr.RVM)was proposed to determine physical relation between RVM parameters and chaotic characteristic of the output of PV power generation.Firstly,the chaotic parameters of PV sequence were computed by pseudo nearest neighbor method and complex autocorrelation method to reconstruct the phase space and to determine the radial range of the Gaussian kernel by RVM prediction method.Secondly,the optimization of RVM parameters was implemented by WOA to improve the generalization ability and the convergence rate of the kernel function to perfect the ultra-short-term forecasting method.Finally,by use of output data of practical PV power generation,the forecasted effect of five typical days was simulated and analyzed.Simulation results show that under different weather situations,the proposed method possesses both satisfied forecasting accuracy and adaptability.