Science and Technology for Energy Transition, Journal Year: 2024, Volume and Issue: 80, P. 9 - 9

Published: Nov. 13, 2024

Wind Power Forecasting (WPF) has gained considerable focus as a crucial aspect of the successful integration and operation wind power. However, due to stochastic unstable nature wind, it poses real challenge effectively analyze correlations among multiple time series data for accurate prediction. In our study, an end-to-end framework called Dynamic Graph structure Spatio-Temporal representation learning (DSTG) is proposed achieve stable power forecasting by constructing graph capture critical features in data. Specifically, Structure Learning (GSL) module introduced dynamically construct task-related correlation matrices via backpropagation mitigate inherent inconsistency randomness Additionally, dual-scale temporal (DTG) further explore implicit spatio-temporal at fine-grained level using different skip connections from constructed Finally, comprehensive experiments are performed on collected Xuji Group (XGWP) dataset, results show that DSTG outperforms state-of-the-art methods 10.12% average root mean square error absolute error, demonstrating effectiveness DSTG. conclusion, model provides promising approach.

Language: Английский