【目的】随着能源消耗的持续增长，以风能为代表的清洁能源装机容量正在稳步提升。然而，新建风电场站数据匮乏导致无法对其进行精确建模，同时还存在伴随场站场景数据发生动态更新，难以捕捉时变特征等问题，给风电功率精准预测带来了极大的挑战。【方法】提出一种针对新建风电场站数据匮乏场景下的风电功率预测方法。首先，采用基于堪培拉距离和动态时间规整算法相结合的相似性度量方法，以高效挖掘多个与目标新建场站相似的源域场站；其次，通过筛选后的源域场站风电数据建立基于多源迁移学习扩张卷积神经网络与双向长短期记忆神经网络（dilated convolutional neural network- bidirectional long short term memory，DCNN-BiLSTM ）的预训练模型，以利用多个相似源域场站数据经验知识对目标新建风电场站进行迁移学习，避免目标新建风电场站过于依附单源域数据；在此基础上，为了深度挖掘时变场景数据对预测结果的影响，提出一种结合在线自适应模块的预测方法，建立考虑数据匹配更新自适应与考虑权重更新自适应的两种模型自进化方式，对在线自适应模块中的基本预测模型与在线模型的输出结果进行加权，实现新建风电场站的短期风电功率预测。【结果】以中国西北某风电场站集群数据为实例验证了所提方法能够优越地对源域风电场站数据进行筛选，同时进行更为精准的功率预测。【结论】文章所提方法为解决有限信息背景下电力数据匮乏问题提供了可行的解决方案，并有望进一步扩展该方法的应用范围。

[Objective] With the continuous growth in energy consumption， the installed capacity of clean energy， particularly wind energy， is steadily increasing. However， the construction of new wind power stations faces significant challenges in accurate wind power prediction owing to the limited data availability and dynamic nature of scene data， which make it difficult to capture time-varying characteristics. [Methods] To address these issue， this study proposes a wind power prediction method for new wind power stations with limited data availability. First， a similarity measurement method combining the Canberra distance and dynamic time warping algorithm is adopted to efficiently identify multiple source-domain wind power stations similar to the new target station. Second， a pre-training model based on a multi-source transfer learning dilated convolutional neural network and bidirectional long short-term memory （dilated convolutional neural network-bidirectional long short-term memory， DCNN-BiLSTM） is established using the wind power data of the selected source domain stations. This approach transfers the experience knowledge of multiple similar source domain stations to the new target wind power station， thereby avoiding over-reliance on single-source domain data. To explore the impact of time-varying scene data on prediction results， a prediction method combining an online adaptive module is proposed. Two self-evolution methods were established considering data-matching update adaptation and weight-update adaptation. The output results of the basic prediction and online models in the online adaptive module were weighted to achieve short-term wind power prediction for the new wind power stations. [Results] The proposed method was verified using data from a wind power station cluster in Northwest China， demonstrating its superior ability to screen source-domain wind power station data and achieve more accurate power predictions. [Conclusions] The method proposed in this article provides a feasible solution to the challenge of scarce power data in under limited information conditions and is expected to broaden the application scope of such methods.