基于改进PSO-LSSVM的风电场短期功率预测

摘要

风速和风电场功率预测是风电场稳定运行及系统调度的重要保障,LSSVM在保持SVM的基础上,可以降低计算复杂性,加快求解速度,为风速及功率预测提供了一个新的研究方向.本研究将最小二乘支持向量机(LSSVM)用于风电场短期风速及风电场功率预测,提出了基于LSSVM的风电场短期风速及功率预测模型,同时建立改进粒子群模型对LSSVM进行参数优化,以内蒙古某风电场实测数据为例进行验证,实例验证表明,改进的PSO-LSSVM模型的预测效果最优.%Wind speed and wind farm power forecasting are an important guarantee of stable operation and system scheduling for wind farm,while LSSVM can reduce the computation complexity,sped up solution speed in the foundation of SVM,provide a new research direction for the wind power forecast.This research uses LSSVM to the wind farm short-term wind speed and power forecasting,and proposes wind farm short-term wind speed and power forecast based on the LSSVM.Simultaneously,establishes the improvement PSO model to carry on the optimization to the LSSVM parameter,and carries on the confirmation test by taking the Inner Mongolian some wind farm measured data as the example,the example confirmation tests indicate that the forecast effect of improved PSO-LSSVM model is optimum.