Inception-embedded attention memory fully-connected network for short-term wind power prediction

Abstract

With the increasing demand for energy in the world today, wind energy has turned out to be an attractive alternative to traditional fossil energy sources because of the characteristics of being clean, non-polluting, and easily accessible. Reliably predicting wind power is vital to improving energy utilization and ensuring the stability of power system operation. However, because of the uncertainty and instability of wind energy, accurately predicting wind power is still challenging. Therefore, this study proposes an Inception-embedded attention memory fully-connected network short-term wind power prediction model, incorporating improved attention mechanisms. As a result, the Inception-embedded attention memory fully-connected network can give reliable wind power predictions. This study utilizes a dataset of about 400 days from Natal and compares the Inception-embedded attention memory fully-connected network with 23 algorithms including EffiientNet, NasNet, and ResNet. The comparison results show that the Inception-embedded attention memory fully-connected network obtains reliable wind power prediction one day ahead and outperforms all other compared algorithms by more than 40% in all evaluation metrics.