A novel algorithm system for wind power prediction based on RANSAC data screening and Seq2Seq-Attention-BiGRU model

Abstract

Accurate wind power prediction plays a crucial role in mitigating the challenges posed by the variability and fluctuations of wind power generation, while also assisting in the regulation of power grid peaks and voltages. This paper introduces a novel algorithm system for wind power prediction, which incorporates RANSAC noise screening and the Seq2Seq-Attention-BiGRU model to enhance prediction accuracy. To address the presence of large-scale noise datasets and the well-established correlation between wind speed and power, we propose the utilization of RANSAC for effective noise screening. Subsequently, the Seq2Seq-Attention wind power prediction model is developed and validated through comparisons with measured data from various wind farms across different seasons. Additionally, the BiGRU error correction model is introduced to further refine the wind power predictions. Comparative analyses between the proposed prediction method and existing forecasting techniques demonstrate the effectiveness of our approach in enhancing prediction accuracy. Specifically, when compared to the LSTM method, the RANSAC-Seq2Seq-Attention-BiGRU forecasting method yielded a remarkable decrease in the RMSE of 26.0%, 43.6%, 46.8%, and 48.4% for the months of January, March, June, and September, respectively. Moreover, the MAE exhibited reductions of 19.7%, 43.4%, 41.0%, and 46.2% for the same respective months.