A new hybrid model for power forecasting of a wind farm using spatial–temporal correlations. (October 2022) - Wen, Songkang; Li, Yanting; Su, Yan
Abstract: This paper develops a new hybrid forecasting model for the hourly power output of multiple turbines (or plants), aimed at making use of both the spatial and temporal correlations of weather factors and power outputs from wind turbines (or plants). To account for the time-varying nature of local pattern and wind propagation, a new clustering algorithm named Kmeans–Hierarchical Clustering (KHC) is first used to adaptively cluster the wind turbines (or plants). Then, singular value decomposition (SVD) is employed to extract the leading components of the wind power of the wind turbines (or plants) in the same cluster. Finally, the support vector regression (SVR) models are built for the leading components and the predictions of the components are transformed into the predictions of the power output of each turbine (or plant) with the inverse SVD. The comparison results show that the proposed model is able to improve the forecast accuracy over the existing short-term forecasting models. Highlights: Considering both spatial and temporal correlation for short-term wind power forecast. Two measures are used to evaluate the similarity of local patterns. A clustering algorithm, Kmeans–hierarchical clustering, is proposed. Singular value decomposition is employed to extract the wind power features.