基于风场景识别的动态风电功率概率预测方法

传统风电功率预测是确定的、静态的、非条件性的,无法代表不同外部状态的发电过程,缺失预测误差的概率性信息。针对上述问题,提出了一种动态的基于风场景识别的风电功率概率预测方法。首先建立基于K means的风场景识别模型,根据风速和风向识别自然风特征,据此划分风电场风况类别。然后针对各风况类别建立基于相关向量机的概率预测模型。在实际预测中,根据实时风况动态调整概率预测模型参数。以中国西北某风电场为例进行验证,结果表明,该方法提高了单点预测精度、概率预测可靠性和技术分数、运行效率,为预测细化建模提供新的解决思路。

Dynamic Wind Power Probabilistic Forecasting Based on Wind Scenario Recognition

Most traditional wind power forecasting methods are deterministic and static without considering external changing conditions as well as probabilistic information of forecasting error. To solve the above problem, a new wind power probabilistic forecasting method is presented based on wind scenario recognition in this paper. Firstly, a wind scenario recognition model is established based on K means clustering algorithm. The natural wind feature is extracted from wind speed and wind direction. Then, the probabilistic forecasting models based on relevance vector machine (RVM) are built for each wind scenario. During real time forecasting, the power generation process in different conditions can be recognized and mapped by adjusting the parameters of the pre established probabilistic forecasting model. Taking a wind farm in Northwest China as an example, the results show that the accuracy of deterministic forecasting, the reliability and skill score of probabilistic forecasting, and forecasting efficiency are improved by the proposed method, which provides a new solution for refined forecast modeling.