Voltage

Engineering

Sustainable energy

Environmental economics

Wind direction

Economics

Computer science

Wind speed

Renewable energy

Geography

Political science

Sustainable development

Environmental science

Artificial intelligence

Transformer

Wind power

Meteorology

Artificial neural network

Electrical engineering

Renewable Energy

Wind power has become one of the essential solutions to renewable energy and sustainable development problems. The accuracy of wind speed forecasts primarily determines the utilization of wind energy. Because many factors affect wind speed, high-precision wind speed prediction is still a practical and challenging task. In this paper, we propose multidimensional spatial-temporal graph neural networks (MST-GNN) for wind speed prediction: (1) establish a transformer-based model named Wind-Transformer on temporal perspective for single-point wind speed prediction with multidimensional data (wind speed, wind direction, temperature, air pressure, etc.), (2) apply graph neural network using Wind-Transformer as a node on spatial view to accurately predict the wind speed at local point by comprehensively aggregating the wind speed of local point and surrounding points. Through comprehensive experiments on open source datasets for wind speed prediction, we demonstrate that our model MST-GNN outperforms the state-of-the-art baselines up to 8.96%. The longer the prediction steps, the more improvement relative to other methods. • Wind power is an essential solution to renewable energy. • Wind speed forecasting primarily determines the utilization of wind energy. • Predicting wind speed with multidimensional data at different locations. • Generating wind prediction parallelly rather than in sequence. • Leveraging the advantages of transformer and graph neural network models.

Promoting wind energy for sustainable development by precise wind speed prediction based on graph neural networks