A short-term wind speed interval prediction method based on WRF simulation and multivariate line regression for deep learning algorithms. (15th April 2022) - Han, Yan; Mi, Lihua; Shen, Lian; Cai, C.S.; Liu, Yuchen; Li, Kai
Highlights: A short-term wind speed interval prediction method is proposed based on WRF simulation and multivariate line regression for deep learning algorithms. Feature collection and decomposition are performed by WRF simulation and VMD algorithm. GAN-CNN-BLSTM-AM is utilized to implement wind speed interval forecasting. The proposed method is superior to other comparative models in single-step and multi-step forecasting. Abstract: The accurate wind speed prediction is of significant importance to decrease the adverse impact of wind randomness on the power systems and help wind power grid-tied. However, the previous studies mainly focus on the point forecast, and the uncertainties of the prediction accuracy have been ignored. Moreover, utilizing WRF numerous variables to construct wind speed prediction interval (PI) based on multivariate line regression theory has rarely been studied. Therefore, a novel wind speed PI method is proposed, including three modules: data collection, decomposition, and interval prediction. Specifically, various variables, such as wind speed, wind direction, temperature, pressure, and relative humidity, are first obtained from weather research and forecasting (WRF) simulation. Secondly, variational mode decomposition (VMD) is applied to decompose these variables and actual wind speed into respective intrinsic mode functions (IMFs). Lastly, generative adversarial network (GAN) captures temporal and spatial correlation features and realizes wind speed PI based on multivariate line regression theory. Four datasets are selected to verify the performance of the proposed model. The experimental results indicate that this novel model achieves better PI accuracy compared to several baseline methods. In the case of wind speed PI of dataset1, the PI normalized average width (PINAW) values improve by 49.53%, 15.21% over VMD-back propagation (BP), VMD-long short-term memory (LSTM) in the seventh-step prediction. In summary, the proposed model outperforms other comparative models due to the lower PINAW and coverage width-based criterion (CWC) with similar PI coverage probability (PICP).