基于EEMD-IGSA-LSSVM的超短期风电功率预测

为了提高风电场输出功率的预测精度,在保证安全操作的前提下,建立了一种基于集合经验模态分解(EEMD)、改进引力搜索算法(IGSA)、最小二乘支持向量机(LSSVM)相结合的风电功率组合预测模型.首先运用EEMD算法将风电功率时间序列分解成一系列复杂度差异明显的子序列;其次利用相空间重构(PSR)对已分解好的子序列进行重构,对重构后的每个子序列分别建立IGSA-LSSVM预测模型,为分析不同核函数构造LSSVM的差异性,建立了8种核函数LSSVM预测模型,利用IGSA算法求解其模型;最后以中国内蒙古地区的某一风电场为算例,仿真及验算结果表明,利用IGSA算法寻优得到的指数径向基核函数核参数和惩罚因子构建的LSSVM模型具有较高的预测准确性;与EEMDWNN,EEMD-PSO-LSSVM等5种常规组合模型相比,所提出的指数径向基核函数的EEMD-IGSA-LSSVM组合模型能有效、准确地进行风电功率预测.

Super-short-Time Wind Power Forecasting Based on EEMD-IGSA-LSSVM

In order to improve the prediction accuracy of the output power of the wind farm under the premise of ensuring safe operation, a combination of wind power forecasting model based on Ensemble Empirical Mode of Decomposition (EEMD), Improved Gravitational Search Algorithm (IGSA) and Least Squares Support Vector Machine (LSSVM) was established. Firstly, the wind power time series was decomposed into a series of subsequences with significant differences in complexity by using EEMD algorithm. Secondly, the decomposed subsequence was reconstructed by the phase space reconstruction (PSR), and then, an IGSA-LSSVM prediction model of each sub-sequence reconstructed was established respectively. In order to analyze the differences of LSSVM which sets up different kernel functions, eight kinds of kernel function LSSVM prediction models were established, and the IGSA algorithm was adopted to solve those models. Finally, taking a wind farm in Inner Mongolia of China as an example, the simulation and calculation results illustrate that LSSVM prediction model based on the exponential radial basis kernel function and penalty factor obtained by using the IGSA algorithm has higher prediction accuracy. Compared with five conventional combined models such as EMD-WNN and EMD-PSO-LSSVM, the combined model EEMD-IGSA-LSSVM of exponential radial basis kernel function mentioned above can forecast wind power in an effective and accurate way.