审计行业专长与股价崩盘风险——基于客户重要性和内部控制水平的视角

针对最小二乘支持向量回归（Least Square Support Vector Regression，LSSVR)建模风电功率时变特性的局限性，提出了一种基于动态集成LSSVR的超短期风电功率预测模型.首先利用风电场监测控制与数据采集（Supervisory Control And Data Acquisition，SCADA）与数值天气预报（Numerical Weather Prediction, NWP）系统的历史数据建立离线单体LSSVR模型库，然后根据预测时段与训练时段NWP序列的相似度从单体LSSVR模型库中动态选择候选集成成员，再后综合考虑正确性与多样性确定集成成员.最后由预测时段与训练时段NWP序列间的相似度分配集成LSSVR成员的权重.通过对湖南省某风电场输出功率进行预测，验证了动态集成LSSVR预测模型的有效性，与持续法、自回归求和移动平均法、单体LSSVR模型、常权重LSSVR组合模型及BPNN动态集成模型相比，动态集成LSSVR模型具有更高的精度，在天气非平稳变化阶段更加明显.

Ultra-short-term Wind Power Prediction Based on Dynamical Ensemble Least Square Support Vector Regression

For the limitation of least square support vector regression (LSSVR) in modeling the time varying feature of wind power, an ultra-short-term wind power prediction (USTWPP) model based on dynamical ensemble LSSVR was proposed. Firstly, the off-line LSSVR model library was created by making use of the historical data which were obtained from Numerical Weather Prediction (NWP) and supervisory control and data acquisition (SCADA) system of wind farm. Then, the candidate members of ensemble LSSVR were selected from off-line LSSVR model library dynamically according to the similarity between the NWP of forecasting period and the NWP of training period. The ensemble members were decided by considering the accuracy and diversity. Finally, the weights of ensemble LSSVR members were assigned according to the similarity between the NWP of training and NWP of prediction period. The validity of the dynamical ensemble LSSVR based predictor was verified by predicting the wind power of a wind farm in Hunan Province. Compared with persistence method (PM), auto regressive integrated moving average (AGIMA), LSSVR, constant weight ensemble LSSVR, and ensemble artificial neural networks (ANN), the dynamical ensemble LSSVR is more accurate, especially when the weather changes severely.