Wake field prediction of a wind farm based on a physics-informed neural network with different spatiotemporal prediction performance improvement strategies

• A PINN-LiDAR framework was developed for dynamic wake prediction of wind farm. • The accuracy of PINN was increased by 20% with two spatial improvement strategies. • Training time of PINN was reduced by 61% with a step-by-step temporal strategy. • Spatio-temporal improvement effect was validated by LiDAR data of a real wind farm. Dynamic wake field information is vital for the optimized design and control of wind farms. Combined with sparse measurement data from light detection and ranging (LiDAR), the physics-informed neural network (PINN) frameworks have recently been employed for forecasting freestream wind and wake fields. However, these PINN frameworks face challenges of low prediction accuracy and long training times. Therefore, this paper constructed a PINN framework for dynamic wake field prediction by integrating two accuracy improvement strategies and a step-by-step training time saving strategy. The results showed that the different performance improvement routes significantly improved the overall performance of the PINN. The accuracy and efficiency of the PINN with spatiotemporal improvement strategies were validated via LiDAR-measured data from a wind farm in Shandong province, China. This paper sheds light on load reduction, efficiency improvement, intelligent operation and maintenance of wind farms.