Learning from ChatGPT: A Transformer-Based Model for Wind Power Forecasting

Wind power forecasting is a crucial aspect of re-newable energy production, as it helps to optimize energy output and ensure grid stability. In recent years, Transformer-based language models such as ChatGPT have been successfully used in natural language processing tasks, but their application in wind power forecasting remains largely unexplored. In this article, we propose using a Transformer model, the core of ChatGPT, to improve the accuracy of wind power forecasting. Using the self-attention mechanism, the developed model can capture the complex temporal relationships in large-scale time series data. Furthermore, the proposed method is evaluated on a test set using various performance metrics. Results show that our model outperforms traditional forecasting models, achieving higher accuracy. Our findings suggest that Transformer-based models have significant potential for improving wind power forecasting accuracy and ultimately contributing to a more sustainable energy future.