## 1 Introduction
A GCN models a turbine network as a graph, which adequately preserves the original spatial associations among the turbines. Following the emergence of the Transformer [15], several studies adopted the self-attention mechanism to capture spatio-temporal correlations for accurate wind speed prediction. For example, the graph attention network and Informer (GAT-Informer) substitutes LSTM and GCNs with temporal attention and spatial attention to enhance the capacity to mine spatio-temporal characteristics [12]. Unfortunately, the Transformer is a sophisticated architecture that requires a vast amount of parameters to be trained. This involves a lot of training data and consumes a considerable amount of computational resources. At present, we witnessed the progress of Large Language Models (LLMs), which drive evolutionary improvements in the field of natural language processing [16; 17]. In time series forecasting, LLMs have also received widespread attention due to two potential advantages. (1) LLMs are powerful representation learners. (2) LLMs are recognized for their exceptional few-shot learning capabilities, making them ideal for real-world scenarios with limited training data. For instance, in [18], researchers presented a unified framework called the Frozen Pre-trained Transformer (FPT) to estimate the time sequence through pre-trained GPT-2. Despite the fact that LLMs can further improve the prediction accuracy, few works use LLMs for wind speed forecasting. If we directly utilize the existing extant LLMs for wind speed prediction, two key difficulties arise:  
_(1) Capturing Spatio-Temporal Correlations in the Turbine Network_: Past studies have shown that capturing the spatio-temporal correlations among turbines can greatly increase the prediction accuracy of wind speed [19; 20]. Unfortunately, the LLM is a decoderarchitecture that only produces future wind speed based on past wind speed series, failing to take spatio-temporal correlations into account. Therefore, it is crucial to mine spatio-temporal characteristics in the LLM. _(2) Optimization of the LLM's Training_: The pre-training of LLMs is general, involving a broad range of tasks without focusing on a specific application named the downstream task. To adapt the downstream task (e.g., wind speed prediction), pre-trained LLMs need to be fine-tuned using a dataset related to the downstream task. At present, most of the available LLM-based time sequence forecasting methods employ a supervised learning strategy, i.e., minimizing the loss of the Mean Squared Error (MSE) to align the true values and the predicted values. Nonetheless, supervised learning typically aims to optimize a specific loss function rather than directly learn the true distribution of the data. As a result, the model focuses more on reducing errors for a particular objective rather than fully understanding the data generation. For instance, in the wind speed prediction, supervised learning improves prediction accuracy by minimizing prediction errors. However, supervised learning overlooks the impact of complex factors such as seasons, geography, and weather, thus failing to fully understand the true generation of wind speed. It is not easy to develop an optimal training strategy to fully train LLMs. To fill up the gaps in existing studies, we propose a novel wind speed prediction architecture entitled Spatio-Temporal GPT-2 United Generative Adversarial Network (STGPT2 UGAN). Specifically, to mine spatio-temporal correlations, we first design a Spatio-Temporal GPT-2 (STGPT-2), including a Spatio-Temporal Block (STB) and Time GPT-2 (TGFT-2). Second, to optimize the training's STGPT-2, we introduce a Generative Adversarial Network (GAN) to offer an adversarial training strategy, which combines unsupervised training and supervised training in a complementary fashion. The contributions of our work are summarized as follows:  
* We devise a novel LLM-based architecture entitled STGPT2 UGAN for wind speed prediction in the turbine network.