Day-ahead Wind Power Forecasting Based on Single Point Clustering

Wind power forecasting is significant for the security and economy of the power system. For the better robustness and accuracy, various clustering-based forecasting methods have been proposed. However, most of them utilize clustering on a daily sample basis usually called day clustering, which mainly exploits daily features of numerical weather prediction (NWP) and meets some limitations. In this paper, a single point clustering method is proposed to enhance the performance of day-ahead forecasting, which is conducted on a point sample basis through K-means algorithm. First, the extraction of clustering features is optimized. Being different from the commonly used clustering features, the high-order features extracted from NWP is used for the proposed method based on stacked denoising autoencoder (SDAE). Then, both the compactness within the single group and the separation among the multiple groups are enhanced, which is realized through choosing the number of clusters to maximize silhouette. In the case study, the proposed method is compared to the conventional day clustering method with two types of features and the single point clustering with the commonly used clustering features of NWP. Through the results, it is shown that the proposed method performs better than the other comparative methods.