The increasing installation rate of wind power poses great challenges to the global power system. In order to ensure the reliable operation of the power system, it is necessary to accurately forecast the wind speed and power of the wind turbines. At present, deep learning is progressively applied to the wind speed prediction. Nevertheless, the recent deep learning methods still reflect the embarrassment for practical applications due to model interpretability and hardware limitation. To this end, a novel deep knowledge-based learning method is proposed in this paper. The proposed method hybridizes pre-training method and auto-encoder structure to improve data representation and modeling of the deep knowledge-based learning framework. In order to form knowledge and corresponding absorbers, the original data is preprocessed by an optimization model based on correlation to construct multi-layer networks (knowledge) which are absorbed by sequence to sequence (Seq2Seq) models. Specifically, new cognition and memory units (CMU) are designed to reinforce traditional deep learning framework. Finally, the effectiveness of the proposed method is verified by three wind prediction cases from a wind farm in Liaoning, China. Experimental results show that the proposed method increases the stability and training efficiency compared to the traditional LSTM method and LSTM/GRU-based Seq2Seq method for applications of wind speed forecasting.

翻译：风力发电装机率的不断增长对全球电力系统构成了巨大挑战。为确保电力系统可靠运行，需要精确预测风力涡轮机的风速和发电功率。目前，深度学习正逐步应用于风速预测领域。然而，现有的深度学习方法因模型可解释性及硬件限制等问题，在实际应用中仍显不足。为此，本文提出了一种新颖的基于深度知识的学习方法。该方法融合预训练技术和自编码器结构，以改善深度知识学习框架的数据表示与建模能力。为形成知识及相应的吸收器，基于相关性优化模型对原始数据进行预处理，构建多层网络（知识），并由序列到序列（Seq2Seq）模型进行吸收。具体而言，设计了新型认知与记忆单元（CMU）以增强传统深度学习框架。最后，通过中国辽宁某风电场的三个风速预测案例验证了所提方法的有效性。实验结果表明，与传统LSTM方法及基于LSTM/GRU的Seq2Seq方法相比，所提方法在风速预测应用中提升了稳定性与训练效率。