A novel and hybrid framework based on generative adversarial network and temporal convolutional approach for wind speed prediction

Abstract

Accurate wind speed forecasting is a precondition for improving grid integration of wind energy. High-dimensional input is required for wind speed prediction models to deliver reliable results. However, owing to the failure of data measuring instruments, the process of obtaining wind speed data encounters various problems. Missing data must be imputed, and dynamic properties must be interpreted accurately in order to forecast successfully. Addressing these, in this study, hybrid forecasting approach using generative adversarial network (GAN) and temporal convolutional network based gated recurrent unit (TCN-GRU) for 5-min, 10-min and 30-min ahead missing value imputation and wind speed forecasting is proposed. TCN is utilized for the feature extraction and GRU forecasts the wind speed. To prove the novelty and performance of the proposed approach, experiments are conducted using data acquired from wind farms located in Bend, and Idalia. The results of the two experiments are compared with robust approaches for imputation as well as forecasting. The proposed hybrid approach is assessed using different performance metrics, and experimental results reveal that the proposed approach's performance for the imputation is improved by 30% and 37% and WSF is improved by 45%, 37% respectively in the two experiments.