基点气象相似聚合的短期风电功率预测方法

提出了一种基点气象相似聚合的短期风电功率预测方法。先以风电功率预测时间点(简称基点)的气象为核心对历史气象记录按灰色关联度进行相似聚合,以突出基点气象对应的风电功率变化规律。再对聚合中历史记录的气象因素按因子分析法降维、求取独立因素,以去除原始气象因素之间的相关性、降低因果关系的非线性度。然后基于径向基神经网络建立"独立因素-风电功率"的映射关系,从而实现风电功率预测。结合实例对此方法进行了仿真,结果表明,此方法预测得到的风电功率,其准确度比基于主成分的径向基神经网络方法的高、比径向基神经网络方法的更高。

Short-Term Wind Power Prediction Method Based on Similar Set of Basis Time Meteorology

A short-term wind power prediction method based on similar set of basis time meteorologyis proposed in this paper. Firstly, the historical meteorological records are selected into a set (here called similar set) by grey relational degree, to obviously expose wind power varying law around the basis time. Then the meteorological factors of historical records in the similar set are reduced in dimension by factor analysis method that produces independent factors, this process eliminates correlation between primary meteorological factors and decreases relation nonlinearity between cause and effect. At last, the mapping function from independent factors to wind power is built based on radial basis function neural network (RBFNN) to realize the prediction of wind power. The proposed method is tested by an actual wind farm. Testing results show: the accuracy of wind power predicted by the proposed method is higher than that by the principal-component-based RBFNN method, and much higher than that by the RBFNN method.