电力建设2026，Vol.47Issue(9)：31-44,14.DOI:10.12204/j.issn.1000-7229.2026.09.003
基于时序大模型的小样本风电功率预测
Small-Sample Wind Power Forecasting Based on A Time-Series Large Model
摘要
Abstract
[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.关键词
风电功率预测/机器学习/大模型/小样本Key words
wind power forecasting/machine learning/large model/small sample分类
信息技术与安全科学引用本文复制引用
刘俊杰,廖文龙,张丞睿,姚贝妮,臧天磊,杨德昌,胡秦然..基于时序大模型的小样本风电功率预测[J].电力建设,2026,47(9):31-44,14.基金项目
国家自然科学基金项目(52377127) This work is supported by National Natural Science Foundation of China(No.52377127). （52377127）
