Probabilistic Prediction of Regional Wind Power Based on Spatiotemporal Quantile Regression

• Published in: 2019 IEEE Industry Applications Society Annual Meeting pp. 1 - 16
• Main Authors: Yu, Yixiao, Han, Xueshan, Yang, Ming, Yang, Jiajun
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.09.2019
• Subjects: Convolution; convolutional neural network; Feature extraction; hybrid neural network; long-short-term memory network; Predictive models; Probabilistic logic; probabilistic prediction; regional wind power prediction; spatiotemporal quantile regression; Wind farms; Wind forecasting; Wind power generation
• ISSN: 2576-702X
• DOI: 10.1109/IAS.2019.8911916

Different from power prediction for a single wind farm, the regional wind power prediction is to predict the total power of multiple wind farms located in the same region. Normally, abundant information on spatiotemporal correlations and nonlinearity is implicated in regional wind farms, and thus selecting the most representative explanatory variables becomes one of the most crucial issues to construct an effective regional wind power prediction model. This paper proposes a spatiotemporal quantile regression (SQR) algorithm to perform short-term nonparametric probabilistic prediction of regional wind power, incorporating the advantages of the hybrid neural network (HNN) and quantile regression (QR). In the approach, the high dimensional input data are reorganized into a feature graph that is ready for feature extraction by the HNN. And the advantages of HNN can therefore be utilized to extract the representative features and construct nonlinear regression models. Meanwhile, by following the QR rules, the model can obtain quantiles and perform probabilistic prediction. By properly addressing the explanatory variable selection issue, the approach provides a specific solution for regional wind generation probabilistic prediction with huge input information. Test results on a region with 10 wind farms demonstrate the effectiveness of the proposed approach.