【目的】主流深度学习模型虽在风电功率预测中表现优异，但高度依赖大量历史数据，在新建风电场或隐私保护等因素导致的小样本场景下，其预测性能往往显著下降。为此，提出了一种基于时序大模型的小样本风电功率预测方法。【方法】首先，采用均值缩放与均匀分箱技术对风电功率时间序列进行逐样本归一化与量化处理，将其转化为离散词元序列，以适配大模型的输入格式。其次，引入文本到文本迁移变换器架构作为核心预测模型，利用其在大规模多领域时序数据上预训练获得的通用表示能力，提取风电功率序列中的深层时序特征。最后，针对特定风电场的小样本数据，采用低秩适应技术对模型进行轻量化微调，在保留预训练知识的同时实现高效适配。【结果】仿真结果表明，在数据稀缺条件下，所提方法在不同季节、不同预测时长、不同区域的风电功率预测任务中的表现均优于多种主流深度学习模型，均方根误差和平均绝对误差较对比模型分别降低了15.18%~33.59%和10.18%~34.94%。【结论】所提方法能够有效利用时序大模型的迁移学习能力，实现小样本条件下的高精度风电功率预测，为新建风电场等数据受限场景下的高精度风电功率预测提供了一种可行且高效的技术路径。

[Objective] Although mainstream deep learning models have achieved excellent performance in wind power forecasting，they rely heavily on large amounts of historical data. In small-sample scenarios，such as newly built wind farms or situations involving privacy protection where data availability is limited，their prediction performance often declines significantly. To address this issue，a small-sample wind power forecasting method based on a time-series large model is proposed. [Methods] First，mean scaling and uniform binning techniques are employed to perform per-sample normalization and quantization of wind power time series，converting them into discrete token sequences so that they match the input format required by large models. Second，a text-to-text transfer transformer architecture is introduced as the core forecasting model. By leveraging the general representation capability obtained through pre-training on large-scale and multi-domain time-series data，the model is able to extract deep temporal features from wind power sequences. Finally，for the small-sample data from a specific wind farm，a lightweight fine-tuning strategy based on low-rank adaptation is adopted to efficiently adapt the model while preserving the knowledge learned during pre-training. [Results] Simulation results demonstrate that under data-scarce conditions，the proposed method outperforms several mainstream deep learning models in wind power forecasting tasks across different seasons，forecasting horizons，and regions. Compared with benchmark models，the root mean square error and mean absolute error are reduced by 15.18%-33.59% and 10.18%-34.94%，respectively. [Conclusions] The proposed method effectively uses the transfer learning capability of time-series large models to achieve high-accuracy wind power forecasting under small-sample conditions. It provides a feasible and efficient technical solution for high-precision wind power forecasting in data-limited scenarios such as newly built wind farms.