Short-term wind speed forecasting using STLSSVM hybrid model

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.

show abstract

Short-term wind speed forecasting using STLSSVM hybrid model

Cited by 5 publications

References 26 publications

Hybridization of hybrid structures for time series forecasting: a review

Hybridization of hybrid structures for time series forecasting: a review

A new approach to short-term wind speed prediction: the prophet model

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

Contact Info

Product

Resources

About