Probabilistic Wind Speed Forecasting based on Minimal Gated Unit and Quantile Regression

Abstract

Abstract High-quality wind speed forecasting (WSF) is of great significance to the power system planning and operation. In this paper, a hybrid method based on Minimal Gated Unit (MGU) and Quantile Regression (QR) is proposed for 2-hour WSF. Firstly, abnormal data is filtered by using the operating mode of SCADA system and Linear Interpolation algorithm is used for missing data imputation. Secondly, this paper embeds conditional quantile as an internal unit of MGU network. We estimate the parameters of MGU network under different quantile conditions, and calculate output under each quantile condition to obtain probabilistic WSF. At last, SCADA data collected from three wind turbines is applied to test the model performance. Both point and interval evaluation criteria are applied to evaluate the performance of models. The results show that the proposed model can obtain multi-step WSF with both point and interval prediction, compared with typical methods, it has higher accuracy in interval predictions and lower computation cost.