BERT4ST:: Fine-tuning pre-trained large language model for wind power forecasting

Abstract

Accurate forecasting of wind power generation is essential for ensuring power safety, scheduling various energy sources, and improving energy utilization. However, the elusive nature of wind, influenced by various meteorological and geographical factors, greatly complicates the wind power forecasting task. To improve the forecasting accuracy of wind power (WP), we propose a BERT-based model for spatio-temporal forecasting (BERT4ST), which is the first approach to fine-tune a large language model for the spatio-temporal modeling of WP. To deal with the inherent characteristics of WP, BERT4ST exploits the individual spatial and temporal dependency of patches and redesigns a set of spatial and temporal encodings. By well analyzing the connection between bidirectional attention networks and WP spatio-temporal data, BERT4ST employs a pre-trained BERT encoder as the backbone network to learn the individual spatial and temporal dependency of patches of WP data. Additionally, BERT4ST fine-tunes the pre-trained backbone in a multi-stage manner, i.e., first aligning the language model with the spatio-temporal data and then fine-tuning the downstream tasks while maintaining the stability of the backbone network. Experimental results demonstrate that our BERT4ST achieves desirable performance compared to some state-of-the-art methods.