Year: 2025
Type: article
Abstract: Accurate forecasting of wind power is essential for maintaining the stability and efficiency of power networks as renewable energy sources become more integrated. This study proposes a multilevel spatial-temporal graph convolution network (MLAGCN) for wind power forecasting. The framework combines a multilevel adaptive graph convolution (MLAGC) and a lightweight temporal transformer (LWTT) to jointly model complex spatial-temporal relationships in wind power data. MLAGC is constructed using three adaptive graphs: a local-aware graph, a global-aware graph, and a structure-aware graph. These components form a flexible graph structure that effectively represents dynamic spatial interactions while LWTT learns short- and long-term sequential patterns. Experiments on real wind farm datasets demonstrated that the proposed model outperforms existing baselines. The model achieved an improved prediction accuracy and generalization, as indicated by a lower score of 43.44, mean absolute error (38.83), root mean square error (48.05) and a forecast loss of 0.22. These results demonstrates the effectiveness of temporal modeling and multilevel attention-based adaptive graph learning for high-resolution wind power forecasting.
Source: Energies
DOI: 10.3390/en19010186
Institution: Seoul National University of Science and Technology
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,137
Awards: G3038139465, G6588179080
OpenAlex ID: W7117760100