Prediction of wind speed can provide a reference for the reliable utilization of wind energy. This study focuses on 1-hour, 1-step ahead deterministic wind speed prediction with only wind speed as input. To consider the time-varying characteristics of wind speed series, a dynamic ensemble wind speed prediction model based on deep reinforcement learning is proposed. It includes ensemble learning, multi-objective optimization, and deep reinforcement learning to ensure effectiveness. In part A, deep echo state network enhanced by real-time wavelet packet decomposition is used to construct base models with different vanishing moments. The variety of vanishing moments naturally guarantees the diversity of base models. In part B, multi-objective optimization is adopted to determine the combination weights of base models. The bias and variance of ensemble model are synchronously minimized to improve generalization ability. In part C, the non-dominated solutions of combination weights are embedded into a deep reinforcement learning environment to achieve dynamic selection. By reasonably designing the reinforcement learning environment, it can dynamically select non-dominated solution in each prediction according to the time-varying characteristics of wind speed. Four actual wind speed series are used to validate the proposed dynamic ensemble model. The results show that: (a) The proposed dynamic ensemble model is competitive for wind speed prediction. It significantly outperforms five classic intelligent prediction models and six ensemble methods; (b) Every part of the proposed model is indispensable to improve the prediction accuracy.

风速的预测可以为风能的可靠利用提供参考。本研究着重于仅以风速为输入的1小时，1步提前确定性风速预测。针对风速序列的时变特性，提出了基于深度强化学习的动态整体风速预测模型。它包括集成学习，多目标优化和深度强化学习以确保有效性。在A部分中，通过实时小波包分解增强的深回波状态网络被用来构建具有不同消失力矩的基本模型。消失力矩的多样性自然保证了基础模型的多样性。在B部分中，采用多目标优化来确定基础模型的组合权重。同步最小化集合模型的偏差和方差，以提高泛化能力。在C部分中，组合权重的非支配解被嵌入到深度强化学习环境中以实现动态选择。通过合理设计强化学习环境，可以根据风速的时变特性在每次预测中动态选择非主导解。使用四个实际风速序列来验证所提出的动态集成模型。结果表明：（a）所提出的动态集成模型在风速预测方面具有竞争力。它明显优于五个经典的智能预测模型和六个集成方法。（b）建议模型的每个部分对于提高预测准确性都是必不可少的。