深度学习模型在风电功率预测中的应用

Application of Deep Learning Models in Wind Power Forecasting

摘要: 风电功率预测(WPF)对风能大规模并网至关重要。然而，由于风能的随机性、不确定性和风电数据的复杂性，风电功率预测一直是一项具有挑战性的工作。本文提出了一种新的量子混合算法，可以同时捕获风电数据的时空特征。该算法包括以下步骤：首先，采用量子编码模型将归一化处理后的风电数据进行量子态的转化，然后，利用谱聚类和自邻接矩阵来构造图的节点和边，用图卷积神经网络高效性地捕捉数据的空间特征，整合邻域信息。最后，我们构造了一个量子图卷积网络，并将时间特征应用于其中。因此，提出了基于注意力机制的量子时空图卷积神经网络(Q-ST-GCN-AM)，通过仿真平台上的实验，验证了该方法的可行性和有效性。实验结果充分证明了该方法在风电功率预测领域的巨大潜力，本文所提的量子混合算法，为风电功率预测提供了新的思路和技术支持，有望推动风能并网技术的进一步发展。

Abstract: Wind power forecasting (WPF) is crucial for the large-scale integration of wind energy into the grid. However, due to the randomness, uncertainty of wind energy, and the complexity of wind power data, wind power forecasting has always been a challenging task. This paper proposes a novel quantum hybrid algorithm that can simultaneously capture the spatiotemporal characteristics of wind power data. The algorithm comprises the following steps: Firstly, a quantum encoding model is employed to transform the normalized wind power data into quantum states. Then, spectral clustering and self-adjacency matrices are utilized to construct the nodes and edges of a graph, enabling the efficient capture of spatial features of the data and the integration of neighborhood information through a graph convolutional neural network. Finally, we construct a quantum graph convolutional network and incorporate temporal features into it. Thus, a Quantum Spatiotemporal Graph Convolutional Neural Network with Attention Mechanism (Q-ST-GCN-AM) is proposed. Experiments conducted on a simulation platform verify the feasibility and effectiveness of this method. The experimental results fully demonstrate the great potential of this method in the field of wind power forecasting. The quantum hybrid algorithm proposed in this paper provides new ideas and technical support for wind power forecasting, promising to drive further development in wind energy grid integration technologies.